Method and device for determining the depth of a pixel of an image by a depth prediction model learned from heterogeneous images

Abstract

Method or device for determining a depth of a pixel by a depth prediction model associated with a vision system comprising a color camera and an infrared camera. Indeed, the model is learned in a learning phase comprising the detection (32) of contours in first and second images acquired by the cameras to obtain first and second single-channel filtered images, the determination (33) of a depth map associated with the second image from the filtered images, the generation (34) of a third image from the first image by adding black pixels then the detection of contours in the third image and the determination (35) of a depth map from the third and second filtered images. The depth prediction model is learned (36) by minimizing an error determined by comparing the depth maps. Figure for the abstract: Figure 3

Description

Title of the invention: Method and device for determining the depth of a pixel of an image by a depth prediction model learned from heterogeneous images Technical field

[0001] The present invention relates to methods and devices for determining a depth by a vision system on board a vehicle, for example in a motor vehicle. The present invention also relates to a method and a device for measuring a distance separating an object from a vehicle carrying a vision system. Technological background

[0002] Many modern vehicles are equipped with so-called AD AS (Advanced Driver-Assistance System). Such AD AS systems are passive and active safety systems designed to eliminate the element of human error in the driving of vehicles of all types. AD AS use advanced technologies to assist the driver while driving and thus improve their performance. AD AS use a combination of sensor technologies to perceive the environment around a vehicle, then provide information to the driver or act on certain vehicle systems.

[0003] There are several levels of ADAS, such as rearview cameras and blind spot sensors, lane departure warning systems, adaptive cruise control and automatic parking systems.

[0004] The AD AS embedded in a vehicle are supplied with data obtained one or more on-board sensors such as, for example, cameras. These cameras make it possible, in particular, to detect and locate other road users or possible obstacles present around a vehicle in order, for example: • to adapt the vehicle's lighting according to the presence of other users; • automatically regulate the vehicle speed; • to act on the braking system in the event of a risk of impact with an object.

[0005] In order to have an extended view of the vehicle's environment, i.e. a three-dimensional scene taking place around the vehicle, a wide-angle camera, i.e. a camera with a wide field of vision, is recommended. Indeed, the use of a wide-angle camera has many advantages over "standard" cameras: • a wider field of vision allowing a larger part of the scene to be captured, which is particularly important in the context of driving, where it is essential to monitor the environment both to the sides and in front of the vehicle, for example, • greater efficiency in perceiving complex environments, such as intersections, tight turns, parking spaces, etc., by minimizing blind spots and providing a more complete view of driving situations, and • increased safety by more easily detecting obstacles, other vehicles, pedestrians and cyclists in areas adjacent to the vehicle.

[0006] Although wide-angle cameras offer many advantages, they can also introduce distortions into the captured images, and these distortions can lead to certain problems such as: • the distortion of straight lines, for example barrel or pincushion, causing curvature of straight lines in an image acquired by the wide-angle camera, making it difficult to estimate the actual distances between objects, particularly towards the edges of the image, • stretching or compressing objects, especially towards the edges of the image, changing the apparent size of objects, which can be problematic when judging the distance or actual size of objects, • changing the proportions of objects, making them larger or smaller than their actual size and more complex to identify, and • the difficulty of rectifying or correcting distortion in post-processing which can be complex and can lead to a loss of information.

[0007] Thus, the processing of an image acquired by a wide-angle camera requires special processing, in particular because of the strong distortion present in the image. Determining a distance separating the vehicle carrying the camera from an object in the scene is then not achievable with the methods commonly used for standard cameras used in certain vision systems.

[0008] Predicting a distance separating an object from a vehicle, i.e. the depth associated with a pixel of an image acquired by a camera, the pixel representing the object, requires high precision in order to feed the AD AS. Predicting a depth associated with a pixel notably calls upon a depth prediction model, the depth prediction model being adapted to the vision system on board the vehicle. Such a prediction model is commonly implemented by a neural network, which is notably learned in a learning phase in order to predict accurate depths in relation to the onboard vision system and the type of environment in which the vehicle is moving. A learning phase then requires data, for example annotated data from libraries or annotated via other more precise onboard systems such as a LIDAR®. However, this type of learning requires a massive accumulation of data or the presence of this other onboard system which the vehicle does not always have. It is then possible to carry out a training phase of the depth prediction model from data acquired by the onboard vision system but the existing solutions are not adapted to the configuration of any onboard vision system in a vehicle.

[0009] In order to be able to use such a vision system in a poorly lit environment, at least one of the cameras of the vision system is for example of the infrared type, that is to say it has a sensor for capturing light waves in the infrared range, for example having a wavelength of between 0.7 pm and 100 pm. Such a camera acquires, for example, single-channel images, that is to say having a single channel, while the cameras commonly used for vision systems are of the RGB type (from the English “Red Green Blue” or in French “Rouge Vert Bleu”, also called RVB), that is to say they acquire multi-channel images comprising, for example, three channels, representative of values ​​associated with light waves captured in the visible range, for example having a wavelength of between 380 nm and 780 nm.The images acquired by these two types of camera are therefore not directly comparable because they are not homogeneous and have sensors that do not capture the same waves. Summary of the present invention

[0010] An object of the present invention is to solve at least one of the problems of the technological background described above.

[0011] Another object of the present invention is to improve the quality of the data resulting from the processing of an image acquired by a vision system, in particular by a depth prediction model implemented by a neural network associated with a stereoscopic vision system comprising an infrared camera.

[0012] Another object of the present invention is to improve road safety, in particular by improving the operational safety of AD AS systems supplied by data obtained from an infrared camera.

[0013] According to a first aspect, the present invention relates to a method for determining a depth of a pixel of an image by a depth prediction model implemented by a convolutional neural network associated with a vision system embedded in a vehicle, the vision system comprising a first camera and a second camera arranged so as to each acquire an image of a three-dimensional scene from different viewpoints, the first camera being configured for color image acquisition and the second camera being configured for infrared image acquisition, the method being implemented by at least one processor, and being characterized in that the depth prediction model is learned in a learning phase comprising the following steps: - receiving a first image acquired by the first camera and a second image acquired by the second camera at the same acquisition time instant; - obtaining a first, respectively second, single-channel filtered image by applying contour detectors to the first, respectively second, image; - determining a first depth map comprising depths associated with a set of pixels of the second image, the depths being predicted with the depth prediction model from the first and second filtered images; - generating a third image from the first image, the third image comprising a set of black pixels obtained by modifying values ​​of a set of pixels of the first image and obtaining a third single-channel filtered image by applying an edge detector to the generated third image; - determining a second depth map comprising depths associated with a set of pixels of the second image, the depths being predicted with the depth prediction model from the third and second filtered images; - learning the depth prediction model by minimizing a loss error determined by comparing the first and second depth maps.

[0014] The contour detectors applied to the images acquired by the cameras of the vision system, the acquired images being heterogeneous, make it possible to make them homogeneous and thus comparable. The depth prediction model is thus applicable to the filtered images and makes it possible to accurately predict the depth of a pixel of an image acquired, in particular by the infrared camera of the stereoscopic vision system, i.e. the distance separating the vehicle carrying the infrared camera from a physical object of the three-dimensional scene associated with this pixel. This learning is carried out from data acquired by the onboard vision system and therefore does not require data annotated by another onboard system or storage of a library of learning images.In addition, the training data is representative of the data received when the stereoscopic vision system is in operation or in production, in fact the training data is representative of real environments in which the vehicle carrying the vision system moves, this training data is therefore particularly relevant.

[0015] According to a variant of the method, the loss error is determined by the following function: With : • The loss error, • D(p) a depth of a pixel p obtained from the first depth map for a pixelp, and • D'(p) a depth of one pixel pt obtained from the second depth map for pixelp.

[0016] According to another variant, one of the contour detectors is a Canny filter.

[0017] According to another variant, the method further comprises an upstream learning phase comprising the following steps: - reception of a fourth image and a fifth image acquired by the first camera and the second camera respectively at the same acquisition time instant; - obtaining a fourth, respectively fifth, single-channel filtered images by applying contour detectors to the fourth, respectively fifth, images; - determining a third depth map comprising depths associated with a set of pixels of the fourth image and a fourth depth map comprising depths associated with a set of pixels of the fifth image, the depths being predicted with the depth prediction model from the fourth and fifth filtered images; - generation of fifth and sixth depth maps from the third and fourth depth maps and extrinsic parameters of the stereoscopic vision system; - generation of sixth and seventh images from the fourth and fifth filtered images and from pixel arrival coordinates of the sixth and fifth depth maps; and - learning the depth prediction model by minimizing a second loss error including: • a reconstruction error determined by comparing the fourth filtered image to the seventh image and comparing the fifth filtered image to the sixth image, and • a consistency error determined by comparing the third and sixth depth maps and by comparing the fourth and fifth depth maps.

[0018]

[0019]

[0020]

[0021] According to a further variant of the method, the fifth and sixth depth maps are generated using the following function: / >( / -,))]) With : • Ps the coordinates of a pixel of the fifth depth map, respectively of the sixth depth map, • 77 a function to go from homogeneous coordinates to pixel coordinates by removing a dimension from a vector, • K' a reverse direction prediction model associated with the second camera, respectively with the first camera, • K a direction prediction model associated with the first camera, respectively with the second camera, • T an extrinsic matrix of the stereoscopic vision system, and • 0 a projection function in the three-dimensional scene of a pixel Pt as a function of its coordinates in the fifth image, respectively fourth image, and of the depth ) which is associated with it in the fourth depth map, respectively third depth map. According to another variant of the method, the direction prediction model associated with a camera is an intrinsic matrix of the camera. According to yet another variant of the method, the direction prediction model is implemented by a neural network. According to a second aspect, the present invention relates to a device for determining a depth by a vision system on board a vehicle, the device comprising a memory associated with at least one processor configured for implementing the steps of the method according to the first aspect of the present invention.

[0022] According to a third aspect, the present invention relates to a vehicle, for example of the automobile type, comprising a device as described above according to the second aspect of the present invention.

[0023] According to a fourth aspect, the present invention relates to a computer program which comprises instructions adapted for executing the steps of the method according to the first aspect of the present invention, in particular when the computer program is executed by at least one processor.

[0024] Such a computer program may use any programming language and be in the form of source code, object code, or intermediate code between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0025] According to a fifth aspect, the present invention relates to a computer-readable recording medium on which is recorded a computer program comprising instructions for carrying out the steps of the method according to the first aspect of the present invention.

[0026] On the one hand, the recording medium may be any entity or device capable of storing the program. For example, the medium may comprise a storage means, such as a ROM memory, a CD-ROM or a microelectronic circuit type ROM memory, or a magnetic recording means or a hard disk.

[0027] Furthermore, this recording medium may also be a transmissible medium such as an electrical or optical signal, such a signal being able to be conveyed via an electrical or optical cable, by conventional or hertzian radio or by self-directed laser beam or by other means. The computer program according to the present invention may in particular be downloaded from an Internet-type network.

[0028] Alternatively, the recording medium may be an integrated circuit in which the computer program is incorporated, the integrated circuit being adapted to perform or to be used in performing the method in question. Brief description of the figures

[0029] Other characteristics and advantages of the present invention will emerge from the description of the particular and non-limiting exemplary embodiments of the present invention below, with reference to the appended figures 1 to 5, in which:

[0030] [Fig-1] schematically illustrates a vision system equipping a vehicle, according to a particular and non-limiting example of embodiment of the present invention;

[0031] [Fig.2] illustrates a flowchart of the different stages of a process of determining a depth of a pixel of an image by a depth prediction model associated with a vision system on board the vehicle of [Fig.l], according to a particular and non-limiting exemplary embodiment of the present invention;

[0032] [Fig.3] illustrates a flowchart of the different stages of a learning process of the depth prediction model used in the method of [Fig.2], according to a particular and non-limiting exemplary embodiment of the present invention;

[0033] [Fig.4] schematically illustrates a device configured to determine a depth of a pixel of an image by a neural network associated with a vision system on board the vehicle of [Fig.l], according to a particular and non-limiting exemplary embodiment of the present invention; and

[0034] [Fig.5] illustrates a flowchart of the different stages of a learning process upstream of the depth prediction model used in the method of [Fig.2], according to a particular and non-limiting exemplary embodiment of the present invention. Description of examples of implementation

[0035] A method and a device for determining a depth of a pixel of an image by a neural network associated with a vision system on board a vehicle will now be described in what follows with joint reference to Figures 1 to 5. The same elements are identified with the same reference signs throughout the description which follows.

[0036] The terms "first(s)", "second(s)" (or "first(s)", "second(s)"), etc. are used in this document by arbitrary convention to enable different elements (such as operations, means, etc.) implemented in the embodiments described below to be identified and distinguished. Such elements may be distinct or correspond to a single element, depending on the embodiment.

[0037] According to a particular and non-limiting example of embodiment of the present invention, a method for determining a depth of a pixel of an image by a depth prediction model implemented by a convolutional neural network associated with a vision system comprising several cameras including a color camera and an infrared camera, these cameras generating images in different and not directly comparable formats.

[0038] Indeed, the depth prediction model is learned in a learning phase comprising the detection of contours in a first and second image acquired by the first color camera and the second infrared camera respectively to obtain a first and a second filtered image each having a channel, the determination of a first depth map associated with the second image from the first and second filtered images, the generation of a third image from the first image by adding black pixels then the detection of contours in the third image and the determination of a depth map from the third and second filtered images.

[0039] The depth prediction model is then learned by minimizing an error determined by comparing the depth maps.

[0040] For the entire description, reception of images or generation of images means the reception of data representative of these images or the generation of data representative of these images.

[0041] [Fig. 1] schematically illustrates a vision system equipping a vehicle, according to a particular and non-limiting exemplary embodiment of the present invention.

[0042] Such an environment 1 corresponds, for example, to a road environment formed of a network of roads accessible to the vehicle 10.

[0043] In this example, the vehicle 10 corresponds to a vehicle with a thermal engine, with an electric motor(s) or even a hybrid vehicle with a thermal engine and one or more electric motors. The vehicle 10 thus corresponds, for example, to a land vehicle such as a car, truck, bus, motorcycle. Finally, vehicle 10 corresponds to an autonomous vehicle or not, that is to say a vehicle traveling according to a determined level of autonomy or under the total supervision of the driver.

[0044] The vehicle 10 advantageously comprises at least two on-board cameras, a first camera 11 and a second camera 12, configured to acquire images of a three-dimensional scene taking place in the environment of the vehicle 10 from separate observation positions. The first camera 11 and the second camera 12 form a stereoscopic vision system when used together as illustrated in [Fig.l]. The first camera 11 forms a monoscopic vision system when used alone, likewise the second camera 12 forms another monoscopic vision system when used alone. The present invention, however, extends to any vision system comprising at least two cameras, for example 2, 3 or 5 cameras.

[0045] The intrinsic parameters of the first camera 11 characterize the transformation which associates, for an image point, subsequently called “point”, its three-dimensional coordinates in the reference frame of the first camera 11 with the pixel coordinates in an image acquired by the first camera 11. These parameters do not change if the first camera 11 is moved. The intrinsic parameters of the first camera 11 include in particular a first focal distance fl associated with the first camera 11.

[0046] The intrinsic parameters of the second camera 12 characterize, for their part, the transformation which associates, for an image point, its three-dimensional coordinates in the reference frame of the second camera 12 with the pixel coordinates in an image acquired by the second camera 12. These parameters do not change if the second camera 12 is moved. The intrinsic parameters of the second camera 12 include in particular a second focal length f2 associated with the second camera 12.

[0047] The distortions, which are due to imperfections in the optical system such as defects in the shape and positioning of the camera lenses, will deflect the light beams and therefore induce a positioning deviation for the projected point compared to an ideal model. It is then possible to complete the camera model by introducing the three distortions which generate the most effects, namely radial, decentering and prismatic distortions, induced by defects in curvature, parallelism of the lenses and coaxiality of the optical axes. In this example, the cameras are assumed to be perfect, that is to say that the distortions are not taken into account, that their correction is processed at the time of image acquisition or at the time of calibration.

[0048] These two cameras 11, 12 are arranged so as to each acquire an image of a scene from a different point of view, the first point of view is for example located on or in the left rearview mirror of the vehicle 10 or at the top of the windshield of the vehicle 10, the second point of view is for example located on or in the right rearview mirror of the vehicle 10 or at the top of the windshield of the vehicle 10. In the case where the two cameras are located at the top of the windshield of the vehicle, they are then placed at a certain distance. In this example, the first camera 11 is located at the top of the windshield of the vehicle 10, the second camera 12 is located in the right rearview mirror of the vehicle 10.

[0049] A first marker is associated with the first camera 11: - the direction of the x axis is defined horizontal and normal to the optical axis Cl of the first camera 11. The distance B separating the optical center of the first camera 11 from the projection of the optical center of the second camera 12 on the horizontal plane passing through the optical center of the first camera 11 is called the reference base (in English “baseline”); - the direction of the y axis is defined vertical and normal to the optical axis Cl of the first camera 11; - the direction of the z axis is defined orthogonal to the directions of the x and y axes. The three axes x, y and z thus form an orthonormal reference frame.

[0050] The optical axis C1 of the first camera 11 and the optical axis C2 of the second camera 12 are not necessarily parallel or even included in the same plane.

[0051] The extrinsic parameters linked to the position of the cameras 11, 12 are the following parameters: - three translations in the x, y and z directions: Tx, Ty and Tz constituting the translation vector T; and - three rotations in the x, y and z directions: 0x, 0y and 0z.

[0052] An extrinsic matrix of the vision system then includes the previously defined extrinsic parameters.

[0053] The extrinsic parameters are determined, for example, during a calibration phase of the stereoscopic vision system comprising the first camera 11 and the second camera 12.

[0054] A main constraint of the stereoscopic vision system used in automobiles is, for example, the large distance between the two cameras. Indeed, to be able to cover a measurement range of 200 meters, the reference base must reach 60cm for the cameras commonly used in this field.

[0055] The two cameras 11, 12 acquire images of a scene located in front of the vehicle 10, the first camera 11 covering only a first acquisition field 13, the second camera 12 covering only a second acquisition field 14 and the two cameras 11, 12 both covering a third acquisition field 15. The first and third acquisition fields 13, 15 thus allow a monoscopic vision of the scene by the first camera 11, the second and third acquisition fields 14, 15 allow a monoscopic vision of the scene by the second camera 12 and the third acquisition field 15 allows a stereoscopic vision of the scene by the stereoscopic vision system composed of the two cameras 11, 12.

[0056] An obstacle 18 is placed in the acquisition field of the cameras, for example in the third acquisition field 15. The presence of the obstacle 18 defines an occlusion field for the stereoscopic vision system composed here of the three fields 16, 17 and 19.

[0057] Among these three fields, field 16 is visible from the second camera 12. The part of the scene present in this field 16 is therefore observable using the monoscopic vision system comprising the second camera 12.

[0058] The field 17 is visible from the first camera 11. The part of the scene present in this field 17 is therefore observable using the monoscopic vision system comprising the first camera 11.

[0059] Finally, field 19 is not visible to any of the cameras. The part of the scene present in this field 19 is therefore not observable.

[0060] According to a particular exemplary embodiment, the field of vision of the second camera 12 covers at least half of the field of vision of the first camera 11.

[0061] It is obvious that it is possible to use such a stereoscopic vision system to take images of scenes located on the sides or behind the vehicle 10 by equipping it with differently placed and oriented cameras.

[0062] The first camera 11 is a color camera, that is to say that it acquires RGB type images (from the English “Red Green Blue”, in French “Rouge Vert Bleu”), representing the scene as it would be, for example, seen by the human eye. Thus, the pixels represent the scene in the visible range. Such an RGB type image comprises a set of pixels, each pixel of this set of pixels being coded on three channels, each channel corresponding to one of the primary colors mentioned above.

[0063] The second camera 12 is an infrared camera, that is to say it acquires images representing the scene from waves emitted by the objects of the scene in the infrared domain, that is to say waves whose wavelength is for example between 700nm (seven hundred nanometers) and 100pm (one hundred micrometers). Such an image comprises a set of pixels, each pixel of this set of pixels being coded on a single channel.

[0064] Such an infrared camera has the advantage of being able to be used both day and night, i.e. the three-dimensional scene observed is illuminated or not. A living being, for example, emits infrared waves through the radiation of its warm body. It is then visible to the infrared camera, also called a thermal camera, even when it is in a shadowy area.

[0065] Each pixel of an acquired image is representative of an object in the three-dimensional scene present in the camera's field of vision. Indeed, a pixel of the acquired image is the smallest visible unit and corresponds to a luminous point resulting from the emission or reflection of light by a physical object present in the three-dimensional scene. When light strikes this object, photons are emitted or reflected, captured by a photosensitive sensor of the camera after passing through its lens. This sensor divides the three-dimensional scene into a grid of pixels. Each pixel records the light intensity at a specific location, thus capturing visual details. The combination of millions of pixels creates an image faithfully representing the physical object observed by the camera. An image point previously presented is thus a point on a surface of an object in the three-dimensional scene.

[0066] The images acquired by the cameras 11, 12 represent views of the same scene taken from different viewpoints, the positions of the cameras being distinct. On this scene are for example: - buildings; - road infrastructure; - other stationary users, for example a parked vehicle; and / or - other mobile users, for example another vehicle, a cyclist or a moving pedestrian.

[0067] According to a particular embodiment, the first camera 11 and / or the second camera 12 is of the “wide-angle” type, a wide-angle camera being for example equipped with a lens designed to acquire an image representative of a three-dimensional scene perceived according to a wider field of vision than that of a standard camera, also sometimes called a panoramic lens. In other words, a wide-angle lens makes it possible to capture a larger portion of the three-dimensional scene taking place in front of or around the wide-angle camera, which is particularly useful in situations where it is necessary to include more elements in the frame of the image acquired by this camera. The angle a of the field of vision of the wide-angle camera is for example equal to 120°, 145°, 180° or 360°, whereas a standard camera offers, for example, an open field of vision following an angle of 45° or less.Such a wide-angle camera is, for example, a camera equipped with mirrors or a "fisheye" camera. Wide-angle lenses have a shorter focal length compared to standard lenses, which makes them suitable for capturing . images of landscapes, architecture, road intersections, or any other subject requiring an extended perspective. Wide-angle cameras, for example, are used to capture immersive and dynamic images with an extended depth of field.

[0068] According to a particular exemplary embodiment, an image acquired by the first camera 11 and / or an image acquired by the second camera 12 comprises a distortion equal to 0.5%, 0.8% or greater than 1%. The measurement of such a distortion corresponds to the determination of a ratio between: - the maximum spacing of a pixel of the image from a straight line of the first three-dimensional scene whose image is a line touching the longest edge of the first image, either at the center of the edge of the image, or at the corners of the edge of the image, and - the length of this edge.

[0069] Commonly, distortion is considered, in the world of photography, as: • negligible if it is less than 0.3%, • not very sensitive if it is between 0.3% or 0.4%, • sensitive if it is between 0.5% and 0.6%, • very sensitive if it is between 0.7% and 0.9%, and • bothersome if it is greater than or equal to 1% or more.

[0070] A barrel distortion is characterized by a positive percentage, while a crescent distortion is characterized by a negative percentage.

[0071] According to a particular exemplary embodiment, a field of view of the first camera 11 covers at least half of a field of view of the second camera 12 and a field of view of the second camera 12 covers at least half of a field of view of the first camera 11. In other words, more than half of the pixels of an image acquired by the first camera 11 correspond to an object of the three-dimensional scene seen by the second camera 12, pixels of an image acquired by the second camera 12 also corresponding to this object of the three-dimensional scene. Similarly, more than half of the pixels of an image acquired by the second camera 12 correspond to an object of the three-dimensional scene seen by the first camera 11, pixels of an image acquired by the first camera 11 also corresponding to this object of the three-dimensional scene.

[0072] The images acquired by the first camera 11 and by the second camera 12 are sent to a computer of a device equipping the vehicle 10 or stored in a memory of a device accessible to a computer of a device equipping the vehicle 10.

[0073] A method for determining a depth by a vision system on board the vehicle 10 is advantageously implemented by the vehicle 10, that is to say by a processor, a computer or a combination of computers of the on-board system of the vehicle 10, for example by the computer(s) in charge of the vision system of the vehicle 10.

[0074] [Fig.2] illustrates a flowchart of the different stages of a method 2 of determining a depth of a pixel of an image by depth prediction model implemented by a convolutional neural network associated with a vision system on board a vehicle, for example in the vehicle 10 of [Fig.l], according to a particular and non-limiting exemplary embodiment of the present invention. Method 2 is for example implemented by a device of the vision system on board the vehicle 10 or by the device 4 of [Fig.4].

[0075] In a step 21, an image acquired by the first camera 11 and an image acquired by the second camera 12 are received.

[0076] In a step 22, contours are detected in each of the received images by applying a characteristic filter to obtain two filtered images each having a single channel. The details of the characteristic filter applied to the images acquired by the cameras of the stereoscopic vision system are notably presented below during the detection step 32 described with regard to the learning method 3.

[0077] In a step 23, depths associated with a set of pixels of one of the received images are determined by the depth prediction model from the two filtered images.

[0078] Each determined depth then corresponds to a distance separating the vehicle 10 or a part of the vehicle 10 from an object of the three-dimensional scene with which a pixel is associated, the determination of a depth of a pixel then corresponding to a measurement of a distance separating an object from the vehicle carrying the vision system.

[0079] If the ADAS uses these depths or distances as input data to determine the distance between a part of the vehicle 10, for example the front bumper, and another user present on the road, the ADAS is then able to determine this distance precisely. For example, if the ADAS has the function of acting on a braking system of the vehicle 10 in the event of a risk of collision with another road user and the distance separating the vehicle 10 from this same road user decreases significantly, then the ADAS is able to detect this sudden approach and act on the braking system of the vehicle 10 to avoid a possible accident.

[0080] According to a particular exemplary embodiment, the learning method 3 further comprises an upstream learning phase. This upstream learning phase is included in an upstream learning method 5, this upstream learning method 5 being described with regard to [Fig.5].

[0081] The upstream learning method 5 is implemented prior to the learning method 3. Indeed, this upstream learning method 5 makes it possible to make the depth prediction by the depth prediction model more reliable under common usage conditions without favoring one or other of the cameras. The learning method 3 then makes it possible to develop the capacity of the stereoscopic vision system to predict depths from the images acquired by the infrared camera, including when images acquired by the color camera are not usable, for example when the latter have too many and / or too large shadow or blackened areas.

[0082] In a step 51, a fourth image and a fifth image acquired by the first camera 11 and the second camera 12 respectively at the same acquisition time instant are received.

[0083] According to a particular exemplary embodiment, the fourth image and fifth image have the same definition, that is to say they comprise the same number of pixels, have the same number of pixels according to their height and the same number of pixels according to their width.

[0084] According to another particular embodiment, the fourth image and fifth image are not of the same definition. An additional step then consists of resizing or cropping them to obtain a fourth image and a fifth image of the same definition.

[0085] In a step 52, a fourth, respectively fifth, single-channel filtered images are obtained by applying contour detectors to the fourth, respectively fifth, images.

[0086] The fourth filtered image has a single channel and is obtained by applying a first edge detector to the fourth image. The fifth filtered image also has a single channel and is obtained by applying a second edge detector to the fifth image.

[0087] Such edge detectors are, for example, a Canny filter. Indeed, a Canny filter is commonly used in image processing to detect edges and is applicable both to an RGB image like the first image and to an image having a single channel like the second image. Only the input parameters of the edge detector differ, the images acquired by the first 11 and the second 12 cameras being heterogeneous.

[0088] Each of the fourth and fifth filtered images then comprises white pixels representative of the identified geometric characteristics, here contours or edges, a white pixel having a value equal to 1, while the rest of the filtered images comprise black pixels, a black pixel having a value of zero.

[0089] In a step 53, a third depth map comprising depths associated with a set of pixels of the fourth image and a fourth depth map comprising depths associated with a set of pixels of the fifth image are determined, the depths being predicted with the depth prediction model from the fourth and fifth filtered images.

[0090] The depth prediction model, implemented by a convolutional neural network, is known to those skilled in the art and is for example presented in the document “UnOS: Unified Unsupervised Optical-flow and Stereo-depth Estimation by Watching Videos” by Yang Wang, Peng Wang, Zhenheng Yang, Chenxu Luo, Yi Yang and Wei Xu published in June 2019, adapted to a stereoscopic vision system comprising cameras whose optical axes are included in the same plane, or in the document “Unifying Flow, Stereo and Depth Estimation” written by Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao and Andréas Geiger, published in July 2023 adapted for any stereoscopic vision system including those comprising cameras whose optical axes are not included in the same plane.

[0091] Thus, a predicted depth is associated with each pixel of a set of pixels of the fourth image, the third depth map comprising the coordinates of each pixel of the set of pixels of the fourth image and an associated depth recorded for example in a look-up table, in a memory accessible to the processor implementing this upstream learning method 5. Similarly, a predicted depth is associated with each pixel of a set of pixels of the fifth image, the fourth depth map comprising the coordinates of each pixel of the set of pixels of the fifth image and an associated depth recorded for example in the look-up table described above.

[0092] In a step 54, fifth and sixth depth maps are determined from the third and fourth depth maps and extrinsic parameters of the stereoscopic vision system.

[0093] The generation of the fifth depth map from the third depth map consists of: • determining spatial coordinates in the three-dimensional scene, in a reference frame associated with the first camera 11, of a point associated with a first pixel of the third depth map from the coordinates of the first pixel in the fourth image and the depth which is associated with this first pixel, its coordinates and depth being recorded in the third depth map. It should be noted that the coordinates of the first pixel in the fourth image comprise two components while the spatial coordinates of the point associated with the first pixel comprise three components; • the determination of spatial coordinates in the three-dimensional scene of the point associated with the first pixel in a reference frame associated with the second camera 12, in other words a change of reference frame from that associated with the first camera 11 to that associated with the second camera 12, using the extrinsic parameters of the stereoscopic vision system; and • the projection of the point associated with the first pixel into the image plane of the second camera 12, this image plane corresponding to that of the fifth image, making it possible to determine the arrival coordinates of the first pixel of the fourth image in an image such as the second camera would have acquired it. Note that the image plane of a camera corresponds to a plane defined in the frame of reference of the camera, normal to the optical axis of the camera and located at the first focal length of the camera. The fifth depth map then includes the arrival coordinates of each first pixel and the depth associated with each first pixel.

[0094] Similarly, generating the sixth depth map from the fourth depth map consists of: • determining spatial coordinates in the three-dimensional scene, in a frame of reference associated with the second camera 12, of a point associated with a second pixel of the fourth depth map from the coordinates of the second pixel in the fifth image and the depth which is associated with this second pixel, its coordinates and depth being recorded in the fourth depth map. It should be noted that the coordinates of the second pixel in the fifth image comprise two components while the spatial coordinates of the point associated with the second pixel comprise three components; • the determination of spatial coordinates in the three-dimensional scene of the point associated with the second pixel in a reference frame associated with the first camera 11, in other words a change of reference frame from that associated with the second camera 12 to that associated with the first camera 11, using the extrinsic parameters of the stereoscopic vision system; and • the projection of the point associated with the second pixel in the image plane of the first camera 11, this image plane corresponding to that of the fourth image, making it possible to determine the arrival coordinates of the second pixel of the fifth image in an image such as the first camera would have acquired it. The sixth depth map then includes the arrival coordinates of each second pixel and the depth associated with each second pixel.

[0095]

[0096]

[0097] According to a particular exemplary embodiment, the fifth and sixth depth maps are generated using the following function: [Math.l] ) ] ) With : • Ps the arrival coordinates of a pixel of the fifth depth map, respectively of the sixth depth map, •77 a function to convert from homogeneous coordinates to pixel coordinates in removing a dimension from a vector, • K' a reverse direction prediction model associated with the second camera (12), respectively to the first camera (11), • K a direction prediction model associated with the first camera (11), respectively to the second camera (12), • T an extrinsic matrix of the stereoscopic vision system, and • 0 a projection function in the three-dimensional scene of a pixel Pt in function of its coordinates in the fifth image, respectively fourth image, and of the depth ) which is associated with it in the fourth card of

[0098]

[0099]

[0100] depths, respectively third depth map. According to a first variant, the direction prediction model associated with a camera is an intrinsic matrix of the camera. Such a variant is particularly applicable in the case of pinhole cameras generating images with little distortion. According to a second variant, the direction prediction model is implemented by a neural network, this direction prediction model being more suitable for wide-angle cameras generating highly distorted images. Such a direction prediction model is known to those skilled in the art, it is notably presented in the document “Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion” written by Igor Vasiljevic, Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Wolfram Burgard, Greg Shakhnarovich and Adrien Gaidon, published in August 2020. The projections and reprojections are then inverse functions obtained from the direction prediction model and are a function of a depth and pixel coordinates. Note that the extrinsic matrix is ​​not the same for the generation of the two images, in fact, a first extrinsic matrix makes it possible to move from a reference frame associated with the first camera 11 to a reference frame associated with the second camera 12 during the generation of the sixth depth map, while a second extrinsic matrix makes it possible to move from a reference frame associated with the second camera 12 to a reference frame associated with the first camera 11 when generating the fifth depth map.

[0101] The projections and reprojections are inverse functions obtained from the direction prediction model and are a function of a depth, such a projection model is notably presented in the document “Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion”.

[0102] In a step 55, sixth and seventh images are generated.

[0103] The sixth image is generated from the fourth filtered image and from the arrival coordinates of a pixel of the sixth depth map, a pixel of the sixth image having as its value that of a pixel of the fourth filtered image and for coordinates in the sixth image the arrival coordinates associated with the pixel of the fourth image.

[0104] The seventh image is generated from the fifth filtered image and from the arrival coordinates of a pixel of the fifth depth map, a pixel of the seventh image having as its value that of a pixel of the fifth filtered image and for coordinates in the seventh image the arrival coordinates associated with the pixel of the fifth image.

[0105] In a step 56, the depth prediction model is learned by minimizing a second loss error comprising: • a reconstruction error determined by comparing the fourth filtered image to the seventh image and comparing the fifth filtered image to the sixth image, and • a consistency error determined by comparing the third and sixth depth maps and by comparing the fourth and fifth depth maps.

[0106] The reconstruction error comprises, for example, a first component determined by comparing pixels of the fourth and seventh images and by comparing pixels of the fifth and sixth images.

[0107] According to a first particular exemplary embodiment, the first component is representative of photometric errors as presented in the document “Digging Into Self-Supervised Monocular Depth Estimation” by Clément Godard, Oisin Mac Aodha, Michael Firman and Gabriel Brostow published in August 2019 and are determined by the following function:

[0108] [Math.2] L^p) - EJ (1-a) • ] / ( p)-Hp) \+a\\-{SSIM(l(pY ï{p) ) ) ]

[0109] With: • L*(p) the first photometric error noted LA p) respectively the second photometric error noted L2(p}, P being a pixel defined by its coordinates in an image, • l(p) a value of pixel p in the fourth image, respectively fifth image, * l(p) a value of pixel p in the seventh image, respectively sixth image, • SSIM a function that takes into account a local structure, and • has a weighting factor depending in particular on the type of environment.

[0110] According to a second particular exemplary embodiment, the reconstruction error comprises a second component further determined by the following function: [YES] [Math.3]

[0112] With: j a second component for a pixel p of the seventh image, respectively of the sixth image, • D(p{^ is a depth of one pixel pt obtained from the fifth depth map, respectively obtained from the sixth depth map; • VF is a parameter matrix; • ° is the order of a smoothing gradient; • an L1 norm of second-order depth gradients is calculated with W =1, and ° =2; • x and V are the dimensions of the images; • P is an environment-dependent hyperparameter; and * P is a value of the pixel pt in the seventh image, respectively sixth image.

[0113] This second function is generally used to deal with discontinuity at the edge of objects (in English “edge aware smoothness”).

[0114] The second loss error also includes a consistency error and is determined by the following function: [Math.4] <£>.,( / >)-Z>6 <p) )2+                  ) 2 )

[0115] With: • The consistency error, * D^p) a depth of a pixel P obtained from the third depth map for a pixelp, • D^p) a depth of a pixel P obtained from the sixth depth map for a pixel P, * D4(p) a depth of one pixel obtained from the fourth depth map for one pixelp, and * D^p) a depth of a pixel p obtained from the fifth depth map for a pixelp. It should be noted that the pixels compared are those with similar or equal coordinates in an acquired image and in a generated image.

[0116] Training the depth prediction model consists of adjusting input parameters of the convolutional neural network associated with the depth prediction model in order to minimize the second loss error, the second loss error comprising the reconstruction error and the consistency error. The second loss error is for example the sum of the reconstruction error and the consistency error.

[0117] Thus, the depth prediction model used for the depth prediction of a pixel of an image acquired by the first camera 11 or by the second camera 12 is made reliable thanks to this learning method.

[0118] [Fig.3] illustrates a flowchart of the different steps of a method for learning the depth prediction model used in a method for determining a depth of a pixel of an image, for example in method 2 of [Fig.2], according to a particular and non-limiting exemplary embodiment of the present invention.

[0119] The learning method 3 is for example implemented by the device on board the vehicle 10 implementing the method for determining a depth by a vision system on board a vehicle or by the device 4 of [Fig.4],

[0120] In a step 31, a first image and a second image are received, the first image being acquired by the first camera 11 at an acquisition time instant and the second image being acquired by the second camera at the same acquisition time instant.

[0121] According to a particular exemplary embodiment, the first image and second image have the same definition, that is to say they comprise the same number of pixels, have the same number of pixels according to their height and the same number of pixels according to their width.

[0122] According to another particular exemplary embodiment, the first image and second image are not of the same definition. An additional step then consists of resizing or cropping them to obtain a first image and a second image of the same definition.

[0123] In a step 32, a first filtered image having a single channel is obtained by applying a first contour detector to the first image and a second filtered image having a single channel is obtained by applying a second contour detector to the second image. Such contour detectors are, for example, a Canny filter. Only the input parameters of the contour detector differ, the images acquired by the first 11 and the second 12 cameras being heterogeneous.

[0124] Each of the first and second filtered images then comprises white pixels representative of the identified geometric characteristics, here contours or edges, a white pixel having a value equal to 1, while the rest of the filtered images comprises black pixels, a black pixel having a zero value.

[0125] In a step 33, a first depth map comprising depths associated with a set of pixels of the second image is determined, the depths being predicted with the depth prediction model from the first and second filtered images.

[0126] The depth prediction model, implemented by a convolutional neural network, is for example one of those presented during step 53 previously described.

[0127] Thus, a predicted depth is associated with each pixel of a set of pixels of the second image, the first depth map comprising the coordinates of each pixel of the set of pixels of the second image and an associated depth recorded for example in a correspondence table, in a memory accessible to the processor implementing this learning method 3.

[0128] In a step 34, a third image is generated from the first image, the third image comprising a set of black pixels obtained by modifying values ​​of a set of pixels of the first image and a third single-channel filtered image is obtained by applying an edge detector to the third generated image.

[0129] As a reminder, as with filtered images, a black pixel is a pixel with a zero value.

[0130] The portion of the third image covered by the set of black pixels varies randomly at each iteration of the steps of the learning method 3, the surface covered by the set of black pixels representing, for example, 10% to 100% of the third image. The set of pixels covers for example 10% of the surface of the third image during a first iteration, to grow at each iteration and cover up to the entire third image. The area of ​​the third image covered by the set of pixels has, for example, a rectangular or circular shape, and is positioned randomly in the third image.

[0131] The addition of black pixels in the third image makes it possible to simulate an image acquired by the first camera in a dark environment. Indeed, the black pixels represent a three-dimensional scene of which a part is not illuminated, the unlit part of the three-dimensional scene corresponding to the area covered by the set of black pixels.

[0132] In a step 35, a second depth map comprising depths associated with a set of pixels of the second image is determined, the depths being predicted with the depth prediction model from the third and second filtered images. This step is thus similar to step 33.

[0133] In a step 36, the depth prediction model is learned by minimizing a loss error determined by comparing the first and second depth maps.

[0134] Training the depth prediction model consists of adjusting input parameters of the convolutional neural network associated with the depth prediction model in order to minimize the previously calculated loss error.

[0135] According to a particular exemplary embodiment, the first error is determined by the following function: [Math.5] L=ï,f(D(p)-D'(p) )2)

[0136] With: • The loss error, • D(p) a depth of one pixel p of the second image obtained from the first depth map, and • D'(p) a depth of pixel p of the second image obtained from the second depth map for pixelp.

[0137] The progressive increase in the size or surface area of ​​the area covered by the set of black pixels makes it possible to teach the depth prediction model to accurately predict a depth of a pixel corresponding to an object in the poorly lit three-dimensional scene. Indeed, the learning of the depth prediction model in conditions of poor lighting of the three-dimensional scene is thus supervised by the depth predictions corresponding to this same well-lit scene. Thus, when only the second camera 12 is able to perceive the objects in a three-dimensional scene, then the predicted depths associated with pixels corresponding to these objects are more precise. The learned depth prediction model is then capable of predicting precise depths in real conditions when the scene is poorly lit, for example when the vehicle 10 is moving at night.

[0138] The learning method 3, as well as the upstream learning method 5, use data acquired by the on-board vision system and therefore do not require data annotated by another on-board system or storage of a library of learning images. In addition, the learning data are representative of the data received when the system is in operation or in production, in fact the learning data are representative of real environments in which the vehicle carrying the vision system moves, this learning data is therefore particularly relevant.

[0139] The contour detectors applied to the images acquired by the cameras of the vision system, the acquired images being heterogeneous, make it possible to make them homogeneous and thus comparable. The depth prediction model is thus applicable to the filtered images and makes it possible to accurately predict the depth of a pixel of an image acquired, in particular by the infrared camera of the stereoscopic vision system, i.e. the distance separating the vehicle carrying the infrared camera from a physical object of the three-dimensional scene associated with this pixel.

[0140] [Fig. 4] schematically illustrates a device 4 configured for the determination of a depth by a vision system on board a vehicle 10, according to a particular and non-limiting exemplary embodiment of the present invention. The device 4 corresponds for example to a device on board the first vehicle 10, for example a computer associated with the stereoscopic vision system.

[0141] The device 4 is for example configured for the implementation of the operations described with regard to figures 1 and 4 and / or steps described with regard to figures 2 and 3. Examples of such a device 4 include, but are not limited to, on-board electronic equipment such as an on-board computer of a vehicle, an electronic calculator such as an ECU (“Electronic Control Unit”), a smartphone, a tablet, a laptop. The elements of the device 4, individually or in combination, can be integrated in a single integrated circuit, in several integrated circuits, and / or in discrete components. The device 4 can be produced in the form of electronic circuits or software (or computer) modules or even a combination of electronic circuits and software modules.

[0142] The device 4 comprises one (or more) processor(s) 40 configured to execute instructions for carrying out the steps of the method and / or for executing the instructions of the software(s) embedded in the device 4. The processor 40 may include integrated memory, an input / output interface, and various circuits known to those skilled in the art. The device 4 further comprises at least one memory 41 corresponding for example to a volatile and / or non-volatile memory and / or comprises a memory storage device which may comprise memory volatile and / or non-volatile, such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic or optical disk.

[0143] The computer code of the embedded software(s) comprising the instructions to be loaded and executed by the processor is for example stored in the memory 41.

[0144] According to various particular and non-limiting embodiments, the device 4 is coupled in communication with other similar devices or systems (for example other computers) and / or with communication devices, for example a TCU (from the English “Telematic Control Unit” or in French “Telematic Control Unit”), for example via a communication bus or through dedicated input / output ports.

[0145] According to a particular and non-limiting exemplary embodiment, the device 4 comprises a block 42 of interface elements for communicating with external devices. The interface elements of the block 42 comprise one or more of the following interfaces: - RF radio frequency interface, for example Wi-Fi® type (according to IEEE 802.11), for example in the 2.4 or 5 GHz frequency bands, or Bluetooth® type (according to IEEE 802.15.1), in the 2.4 GHz frequency band, or Sigfox type using UBN (Ultra Narrow Band) radio technology, or LoRa in the 868 MHz frequency band, LTE (Long-Term Evolution), LTE-Advanced; - USB interface (from the English “Universal Serial Bus” or “Universal Serial Bus” in French); HD MI interface (from the English “High Definition Multimedia Interface” or “High Definition Multimedia Interface” in French); - LIN interface (from the English “Local Interconnect Network”).

[0146] According to another particular and non-limiting exemplary embodiment, the device 4 comprises a communication interface 43 which makes it possible to establish communication with other devices (such as other computers of the on-board system) via a communication channel 430. The communication interface 43 corresponds for example to a transmitter configured to transmit and receive information and / or data via the communication channel 430. The communication interface 43 corresponds for example to a wired network of the CAN (from the English “Controller Area Network” or in French “Réseau de contrôles”) type, CAN FD (from the English “Controller Area Network Flexible Data-Rate” or in French “Réseau de contrôles à débit de données flexible”), FlexRay (standardized by the ISO 17458 standard) or Ethernet (standardized by the ISO / IEC 802-3 standard).

[0147] According to a particular and non-limiting exemplary embodiment, the device 4 can provide output signals to one or more external devices, such as a display screen 440, touch-sensitive or not, one or more speakers 450 and / or other peripherals 460 via the output interfaces 44, 45, 46 respectively. According to a variant, one or other of the external devices is integrated into the device 4.

[0148] Of course, the present invention is not limited to the exemplary embodiments described above but extends to a method for measuring a distance separating an object from a vehicle carrying a vision system acquiring images which would include secondary steps without departing from the scope of the present invention. The same would apply to a device configured for implementing such a method.

[0149] The present invention also relates to a vehicle, for example an automobile or more generally an autonomous land-based motor vehicle, comprising the device 4 of [Fig.4].

Claims

1. Claims Method for determining a depth of a pixel of an image by a depth prediction model implemented by a convolutional neural network associated with a vision system embedded in a vehicle (10), the vision system comprising a first camera (11) and a second camera (12) arranged so as to each acquire an image of a three-dimensional scene from different viewpoints, the first camera being configured for the acquisition of color images and the second camera (12) being configured for the acquisition of infrared images, said method being implemented by at least one processor, and being characterized in that the depth prediction model is learned in a learning phase comprising the following steps: - reception (31) of a first image acquired by the first camera (11) and of a second image acquired by the second camera (12) at the same acquisition time instant; - obtaining (32) a first, respectively second, single-channel filtered image by applying contour detectors to the first, respectively second, image; - determining (33) a first depth map comprising depths associated with a set of pixels of the second image, the depths being predicted with the depth prediction model from the first and second filtered images; - generation (34) of a third image from the first image, the third image comprising a set of black pixels obtained by modifying the values ​​of a set of pixels of the first image and obtaining a third single-channel filtered image by applying a contour detector to the third generated image; - determining (35) a second depth map comprising depths associated with a set of pixels of the second image, the depths being predicted with the depth prediction model from the third and second filtered images; - learning (36) the depth prediction model by minimizing a loss error determined by comparing the first and second depth maps.

2. Method according to claim 1, for which the loss error is determined by the following function: t=EP(O(p)-D(p))2) With: • L the loss error, • I)(p) a depth of a pixelp obtained from the first depth map for a pixel p, and • D'(p) a depth of a pixel p obtained from the second depth map for the pixel p.

3. A method according to claim 1 or 2, wherein one of said edge detectors is a Canny filter.

4. Method according to one of claims 1 to 3, which further comprises an upstream learning phase comprising the following steps: - receiving (51) a fourth image and a fifth image acquired by the first camera (11) and the second camera (12) respectively at the same acquisition time instant; - obtaining (52) a fourth, respectively fifth, single-channel filtered images by applying contour detectors to the fourth, respectively fifth, images; - determining (53) a third depth map comprising depths associated with a set of pixels of the fourth image and a fourth depth map comprising depths associated with a set of pixels of the fifth image, the depths being predicted with the depth prediction model from the fourth and fifth filtered images;- determination (54) of fifth and sixth depth maps from the third and fourth depth maps and extrinsic parameters of the stereoscopic vision system; - generation (55) of sixth and seventh images from the fourth and fifth filtered images and from arrival coordinates of pixels of the sixth and fifth depth maps; and; - learning (56) the depth prediction model by minimizing a second loss error comprising: • a reconstruction error determined by comparing the fourth filtered image to the seventh image and comparing the fifth filtered image to the sixth image, and • a consistency error determined by comparing the third and sixth depth maps and by comparing the fourth and fifth depth maps.

5. The method of claim 4, wherein the fifth and sixth depth maps are generated using the following function: ^(^(pjA-.Dfp,))]) With: • Ps the coordinates of a pixel of the fifth depth map, respectively of the sixth depth map, •77 a function for going from homogeneous coordinates to pixel coordinates by removing one dimension of a vector, • K' an inverse direction prediction model associated with the second camera (12), respectively with the first camera (11), • K a direction prediction model associated with the first camera (11), respectively with the second camera (12), • T an extrinsic matrix of the stereoscopic vision system, and • 0 a projection function in the three-dimensional scene of a pixel Pt as a function of its coordinates in the fifth image, respectively fourth image, and of the depth ) which is associated with it in the fourth depth map, respectively third depth map.

6. The method of claim 5, wherein the direction prediction model associated with a camera is an intrinsic matrix of the camera.

7. The method of claim 5, wherein the direction prediction model is implemented by a neural network.

8. Computer program comprising instructions for implementing the method according to any one of the preceding claims, when these instructions are executed by a processor.

9. 30 Device (4) for determining a depth by a vision system on board a vehicle (10), said device (4) comprising a memory (41) associated with at least one processor (40) configured for implementing the steps of the method according to any one of claims 1 to 7.

10. Vehicle (10) comprising the device (4) according to claim 9.

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