Luminance distribution estimation device and luminance distribution estimation program

Abstract

To provide a luminance distribution estimation device and a luminance distribution estimation program capable of estimating a luminance distribution at a measurement point different from the actual measurement point without being affected by blind spots at the time of imaging.SOLUTION: A luminance distribution estimation device 10 comprises: a first acquisition unit 11A which acquires a first luminance distribution indicating the distribution of luminance obtained by measurement at a predetermined first measurement point, and a first contribution matrix indicating the magnitude of the influence of daylight on the first measurement point; a derivation unit 11B which derives a light intensity distribution on an incident surface where daylight enters, by using the first luminance distribution and the first contribution matrix, as one reproduced by a sum of multiple functions, each having a convex shape when graphed and forming a curved surface on which likelihood varies; a second acquisition unit 11C which acquires a second contribution matrix indicating the magnitude of the influence of daylight on a second measurement point different from the first measurement point; and an estimation unit 11D which estimates, using the light intensity distribution and the second contribution matrix, a second luminance distribution indicating the luminance distribution at the second measurement point.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a luminance distribution estimation device and a luminance distribution estimation program. [Background technology]

[0002] When creating an indoor lighting environment, it is important to appropriately and simply take into account the influence of external light that enters through windows, which contributes significantly to the overall lighting environment. In recent years, there has been a method for measuring the luminance distribution in an area that includes windows using a camera or other imaging device, and then controlling the building's lighting, blinds, etc. based on the visual environment index calculated from the measurement.

[0003] Conventionally, the following technologies have been used to control lighting in buildings.

[0004] Patent Document 1 discloses a lighting control system that aims to enable lighting control suited to the perception of occupants in a lighting space with windows or the like.

[0005] This lighting control system includes a lighting fixture that illuminates an indoor lighting space that has a window surface that transmits light between it and the outdoors, and multiple imaging means that capture images of a person's field of view from different directions within the lighting space.The lighting control system also includes a luminance distribution measurement means that measures the luminance distribution in the images captured by each imaging means, and a window surface luminance calculation means that measures the luminance of the window surface in the images captured by each imaging means.The lighting control system also includes a field of view detection means that detects the field of view of a person present in the images captured by the imaging means, and a control means that dims the lighting fixture based on the luminance distribution in the images captured of the person's field of view that have been detected and the luminance of the window surface.

[0006] However, this technology requires the use of multiple image capture devices. In this case, because the brightness of light sources such as windows varies greatly depending on the viewing angle, it is desirable to install the image capture devices at the line of sight of building users (e.g., office workers) in order to obtain an index that accurately represents the human visual environment. However, installation locations are limited because they would be a nuisance to users. For example, they would have to be hung from the ceiling, etc., which would be far from the line of sight. In addition, they have poor design qualities and may give users a psychological sense of oppression, as if they are being monitored. Furthermore, installing them at each user's line of sight is expensive.

[0007] As a technology that can be applied to solve this problem, Patent Document 2 discloses a lighting control system that aims to enable lighting control that creates a comfortable illuminated space for people, without the need to install multiple cameras in all directions.

[0008] This lighting control system includes a visible image acquisition unit that acquires a visible image, and a viewpoint conversion unit that receives a visual field of a person and converts the visible image acquired by the visible image acquisition unit into a visible image of the visual field of the person from the human viewpoint. The lighting control system also includes a luminance distribution calculation unit that calculates a luminance distribution of the visual field of the person based on the visible image from the human viewpoint, a dimming control unit that controls dimming of lighting fixtures based on the luminance distribution, and an image storage unit that stores the visible images acquired by the visible image acquisition unit together with their capture times. The lighting control system also includes a scattering property estimation unit that extracts multiple visible images captured at different times from the visible images acquired by the visible image acquisition unit and the visible images stored in the image storage unit, and estimates scattering properties for each of multiple partial regions constituting the visible image acquired by the visible image acquisition unit based on the multiple visible images captured at different times, and the luminance distribution calculation unit corrects the luminance distribution based on the scattering properties. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Japanese Patent Application Laid-Open No. 2010-9874 [Patent Document 2] Patent No. 6863475 Summary of the Invention [Problem to be solved by the invention]

[0010] However, the technology disclosed in Patent Document 2 has the problem that, since a visible image captured at a certain position is converted into a visible image from a human viewpoint, it is not possible to obtain a brightness distribution for areas that are blind spots during the above-mentioned capture.

[0011] The present disclosure has been made in consideration of the above circumstances, and aims to provide a luminance distribution estimation device and a luminance distribution estimation program that can estimate the luminance distribution at a measurement point different from the actual measurement point without being affected by blind spots during shooting. [Means for solving the problem]

[0012] The luminance distribution estimation device of the present invention described in claim 1 comprises: a first acquisition unit that acquires a first luminance distribution that indicates the distribution of luminance obtained by measurement at a predetermined first measurement point, and a first contribution matrix that indicates the magnitude of the influence of daylight on the first measurement point; a derivation unit that uses the first luminance distribution and the first contribution matrix to derive a luminance intensity distribution on an incident surface onto which the daylight is incident, as a distribution that is reproduced as the sum of multiple functions that, when graphed, have an upward convex shape and produce a curved surface with varying likelihood; a second acquisition unit that acquires a second contribution matrix that indicates the magnitude of the influence of the daylight on a second measurement point different from the first measurement point; and an estimation unit that uses the luminance intensity distribution and the second contribution matrix to estimate a second luminance distribution that indicates the distribution of luminance at the second measurement point.

[0013] According to the luminance distribution estimation device of the present invention as set forth in claim 1, a first luminance distribution indicating the distribution of luminance obtained by measurement at a predetermined first measurement point and a first contribution matrix indicating the magnitude of the influence of daylight on the first measurement point are obtained, and the luminance intensity distribution on the incident surface onto which daylight is incident is derived using the first luminance distribution and the first contribution matrix, as a distribution reproduced by the sum of multiple functions that, when graphed, have an upward convex shape and create a curved surface with varying likelihood, and a second contribution matrix indicating the magnitude of the influence of daylight on a second measurement point different from the first measurement point is obtained, and the luminance intensity distribution and the second contribution matrix are used to estimate the second luminance distribution indicating the distribution of luminance at the second measurement point.As a result, there is no need to use a visible image at the second measurement point, and it is possible to estimate the luminance distribution at a measurement point different from the actual measurement point without being affected by blind spots during photography.

[0014] A luminance distribution estimation device according to the present invention, as set forth in claim 2, is the luminance distribution estimation device as set forth in claim 1, in which the plurality of functions are a plurality of two-dimensional Gaussian functions.

[0015] According to the brightness distribution estimation device of the present invention as set forth in claim 2, by using a plurality of two-dimensional Gaussian functions as the plurality of functions, it is possible to estimate the brightness distribution more easily compared to a case where a plurality of two-dimensional Gaussian functions are not applied as the plurality of functions.

[0016] A luminance distribution estimation device according to the present invention, as set forth in claim 3, is the luminance distribution estimation device as set forth in claim 2, in which the plurality of two-dimensional Gaussian functions are two two-dimensional Gaussian functions.

[0017] According to the luminance distribution estimation device of the present invention as set forth in claim 3, by using two two-dimensional Gaussian functions as the plurality of two-dimensional Gaussian functions, it is possible to estimate the luminance distribution more easily compared to the case where three or more two-dimensional Gaussian functions are used as the plurality of two-dimensional Gaussian functions.

[0018] A luminance distribution estimation device according to the present invention, as set forth in claim 4, is the luminance distribution estimation device as set forth in claim 1 or claim 2, wherein the first acquisition unit further acquires a third contribution matrix indicating the magnitude of the influence of illumination light by artificial lighting on the first measurement point when the dimming rate of the illumination light is 100%, and the dimming rate; the derivation unit derives the emission intensity distribution using the first luminance distribution, the first contribution matrix, the third contribution matrix, and the dimming rate; the second acquisition unit further acquires a fourth contribution matrix indicating the magnitude of the influence of the illumination light on the second measurement point; and the estimation unit estimates the second luminance distribution using the emission intensity distribution, the second contribution matrix, the fourth contribution matrix, and the dimming rate.

[0019] According to the luminance distribution estimation device of the present invention as set forth in claim 4, a third contribution matrix indicating the magnitude of the influence that the artificial lighting has on the first measurement point when the dimming rate of the illumination light is 100%, and the dimming rate are further acquired, an emission intensity distribution is derived using the first luminance distribution, the first contribution matrix, the third contribution matrix, and the dimming rate, and a fourth contribution matrix indicating the magnitude of the influence that the artificial lighting has on the second measurement point is further acquired, and a second luminance distribution is estimated using the emission intensity distribution, the second contribution matrix, the fourth contribution matrix, and the dimming rate, thereby making it possible to more accurately estimate the luminance distribution at a measurement point different from the actual measurement point when artificial lighting is present in addition to daylight.

[0020] A luminance distribution estimation program according to the present invention, as set forth in claim 5, causes a computer to execute the following processes: acquire a first luminance distribution indicating the distribution of luminance obtained by measurement at a predetermined first measurement point, and a first contribution matrix indicating the magnitude of the influence of daylight on the first measurement point; use the first luminance distribution and the first contribution matrix to derive a luminance intensity distribution on an incident surface onto which the daylight is incident, as a distribution reproduced by the sum of multiple functions that, when graphed, have an upward convex shape and produce a curved surface with varying likelihood; acquire a second contribution matrix indicating the magnitude of the influence of the daylight on a second measurement point different from the first measurement point; and use the luminance intensity distribution and the second contribution matrix to estimate a second luminance distribution indicating the distribution of luminance at the second measurement point.

[0021] According to the luminance distribution estimation program of the present invention as set forth in claim 5, a first luminance distribution indicating the distribution of luminance obtained by measurement at a predetermined first measurement point and a first contribution matrix indicating the magnitude of the influence of daylight on the first measurement point are obtained, and the first luminance distribution and the first contribution matrix are used to derive the luminance intensity distribution on the incident surface onto which daylight is incident as a sum of multiple functions that, when graphed, have an upward convex shape and create a curved surface with varying likelihood, and a second contribution matrix is ​​obtained that indicates the magnitude of the influence of daylight on a second measurement point different from the first measurement point, and the luminance distribution indicating the distribution of luminance at the second measurement point is estimated using the luminance intensity distribution and the second contribution matrix.As a result, there is no need to use a visible image at the second measurement point, and it is possible to estimate the luminance distribution at a measurement point different from the actual measurement point without being affected by blind spots during photography. [Effects of the Invention]

[0022] As described above, according to the present invention, it is possible to estimate the luminance distribution at a measurement point different from the actual measurement point without being affected by blind spots during imaging. [Brief explanation of the drawings]

[0023] [Figure 1] FIG. 1 is a block diagram showing an example of a hardware configuration of a luminance distribution estimation apparatus according to an embodiment. [Figure 2] FIG. 10 is a diagram for explaining a contribution matrix according to an embodiment, and is a cross-sectional view showing an example of a combination of light traveling directions and measurement points when there are two light sources and two measurement points. [Figure 3] FIG. 1 is a block diagram illustrating an example of a functional configuration of a luminance distribution estimation device according to an embodiment. [Figure 4] FIG. 10 is a schematic diagram showing an example of a discrete light emission intensity distribution on a window surface. [Figure 5A] 1 is a graph showing an example of a two-dimensional Gaussian function. [Figure 5B] 1 is a graph showing an example of a two-dimensional Gaussian function. [Figure 6] FIG. 10 is a diagram showing an example of a luminance distribution displayed on a window surface according to equation (3). [Figure 7] FIG. 2 is a schematic diagram illustrating an example of a configuration of a building-related information database according to the embodiment. [Figure 8] FIG. 2 is a schematic diagram showing an example of the configuration of a luminance distribution information database according to the embodiment. [Figure 9] 10 is a flowchart illustrating an example of a luminance distribution estimation process according to the embodiment. [Figure 10] FIG. 10 is a front view showing an example of a configuration of an initial information input screen according to the embodiment. [Figure 11] FIG. 1 is a diagram for explaining the prior art, and is a graph showing an example of the light source position of direct sunlight in the multi-phase method, as an example of discretizing the light source position. DETAILED DESCRIPTION OF THE INVENTION

[0024] Hereinafter, an example of an embodiment of the present invention will be described in detail with reference to the drawings. In this embodiment, the present invention will be described as being applied to a luminance distribution estimation device that estimates a luminance distribution when a desired position in a room provided in a predetermined building is set as a measurement point.

[0025] First, the configuration of a luminance distribution estimation device 10 according to this embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the hardware configuration of the luminance distribution estimation device 10 according to this embodiment. Examples of the luminance distribution estimation device 10 include information processing devices such as a personal computer and a server computer.

[0026] 1, a luminance distribution estimation device 10 according to this embodiment includes a CPU (Central Processing Unit) 11, a memory 12 serving as a temporary storage area, a nonvolatile storage unit 13, an input unit 14 such as a keyboard and a mouse, a display unit 15 such as a liquid crystal display, a medium read / write device (R / W) 16, and a communication interface (I / F) unit 18. The CPU 11, memory 12, storage unit 13, input unit 14, display unit 15, medium read / write device 16, and communication interface (I / F) unit 18 are connected to one another via a bus B. The medium read / write device 16 reads information written in a recording medium 17 and writes information to the recording medium 17.

[0027] The storage unit 13 is realized by an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like. The storage unit 13, which serves as a storage medium, stores a luminance distribution estimation program 13A and a physical lighting simulation program 13B. The luminance distribution estimation program 13A is stored in the storage unit 13 by setting a recording medium 17 on which the program 13A is written in the medium reading and writing device 16 and having the medium reading and writing device 16 read the program 13A from the recording medium 17. The physical lighting simulation program 13B is also stored in the storage unit 13 by setting a recording medium 17 on which the program 13B is written in the medium reading and writing device 16 and having the medium reading and writing device 16 read the program 13B from the recording medium 17. The CPU 11 appropriately reads the luminance distribution estimation program 13A and the physical lighting simulation program 13B from the storage unit 13, expands them in the memory 12, and sequentially executes the processes of the programs.

[0028] In this embodiment, the existing program Radiance is used as the physical lighting simulation program 13 B. However, the present invention is not limited to this, and other existing programs having similar functions or dedicated programs may be used as the physical lighting simulation program 13 B.

[0029] Furthermore, a building-related information database 13C and a luminance distribution information database 13D are stored in the storage unit 13. The building-related information database 13C and the luminance distribution information database 13D will be described in detail later.

[0030] On the other hand, the communication I / F unit 18 is connected to an imaging device 30 provided in a room whose luminance distribution is to be estimated (hereinafter referred to as an "estimation target room").

[0031] In this embodiment, a surveillance camera that has been previously installed on the ceiling of the room to be estimated is used as the imaging device 30. This eliminates the need to install a new imaging device in the room to be estimated, which does not increase costs and also eliminates the need to provide the installation space that would be required to install a new imaging device. However, this is not a limitation, and it goes without saying that a new dedicated imaging device 30 may be installed in the room to be estimated. Furthermore, in this embodiment, a device that captures color images is used as the imaging device 30, but this is not a limitation. For example, a device that captures monochrome images may be used as the imaging device 30.

[0032] The luminance distribution estimation device 10 according to this embodiment uses a contribution matrix, which indicates the magnitude of the influence of daylight on the measurement points and is obtained by a conventionally known physical lighting simulation program (physical lighting simulation program 13B in this embodiment). Here, the contribution matrix will be explained.

[0033] When using conventional physical lighting simulation programs such as Radiance, the calculation results for daylight vary depending on the weather and time of day. For this reason, simulations must be run for each set of conditions, but a multi-phase method is available to speed up this calculation.

[0034] In the multi-phase method, a calculation result matrix (light source position x calculation point) for each calculation point per discretized reference light source intensity is created in advance, and by multiplying and adding this with the light source intensity vector (light source position) according to the weather and time, the result for the calculation point under the specified weather and time conditions can be obtained instantaneously. The above calculation result matrix indicates the magnitude of the impact that daylight has on the measurement point, and this calculation result matrix is ​​called the contribution matrix M.

[0035] There are several variations in how the contribution matrix M is used. For example, if the light sources are skylight and direct sunlight, the photometric quantities of the measurement point can be calculated using the contribution matrix and the luminous intensity distribution of the light source as shown in the following equation (1). In equation (1), "Direct Daylight Coefficients" is the contribution matrix of direct daylight, and "Sky Daylight Coefficients" is the contribution matrix of skylight. Also, in equation (1), "Sun vecor" is the luminous intensity distribution of direct daylight, "Sky vecor" is the luminous intensity distribution of skylight, and "sensor values" are the photometric quantities of the measurement point. Furthermore, in equation (1), m is the number of pixels (number of measurement points) in the sensor of the imaging device, and n is the number of elements in the luminous intensity distribution of direct daylight and skylight.

[0036]

number

[0037] Figure 11 shows an example of the light source position discretization for direct sunlight in the multi-phase method (Source: "Standard daylight coefficient model for dynamic daylighting simulations," [online], [Retrieved March 19, 2024], Internet) <URL:https: / / www.researchgate.net / publication / 228683789_Standard_daylight_coefficient_model_for_dynamic_daylighting_simulations> "). The actual sun can take any position within the thick frame, but it is discretized, and the calculation point value is found only at the positions marked with a + sign. The calculation results of multiple light source positions (marked with a + sign) adjacent to the actual sun position are weighted averaged. This averaging process sacrifices spatial resolution (edge ​​strength), but ensures the accuracy of the physical quantities. In this specification, the above direct sunlight is also referred to as "direct daylight."

[0038] FIG. 2 is a diagram illustrating the contribution matrix M according to this embodiment, and is a cross-sectional view showing an example of a combination of the light propagation direction and the measurement points when there are two light sources and two measurement points.

[0039] The luminance distribution estimation device 10 according to this embodiment uses a contribution matrix M by regarding the light source as only a window surface or a window surface having a window device such as a blind.

[0040] For example, in the example shown in Figure 2, the following two equations can be established. Note that f in the following equations ij is the illuminance given to the measurement point i by the light source j of the reference intensity, and L j is the emission intensity of light source j, and E i is the illuminance at measurement point i.

[0041] f 11 ×L1+f 21 ×L2=E1

[0042] f 12 ×L1+f 22×L2=E2

[0043] As an example, as shown in Figure 2, light emitted from a certain light source is reflected repeatedly and finally reaches a certain measurement point, where the illuminance is f ij and f ij is generalized and converted into a matrix to form the contribution matrix M.

[0044] Next, the functional configuration of the luminance distribution estimation device 10 according to this embodiment will be described with reference to Fig. 3. Fig. 3 is a block diagram showing an example of the functional configuration of the luminance distribution estimation device 10 according to this embodiment.

[0045] 3, the luminance distribution estimation device 10 according to this embodiment includes a first acquisition unit 11A, a derivation unit 11B, a second acquisition unit 11C, and an estimation unit 11D. The CPU 11 of the luminance distribution estimation device 10 executes a luminance distribution estimation program 13A, thereby functioning as the first acquisition unit 11A, the derivation unit 11B, the second acquisition unit 11C, and the estimation unit 11D.

[0046] The first acquisition unit 11A according to this embodiment acquires a first luminance distribution indicating the distribution of luminance obtained by measurement at a predetermined first measurement point, and a first contribution matrix indicating the magnitude of the influence of daylight on the first measurement point. In this embodiment, the first luminance distribution is acquired from the image capture device 30. In this embodiment, the first contribution matrix is ​​acquired using a physical lighting simulation program 13B.

[0047] Furthermore, the derivation unit 11B according to this embodiment derives the emission intensity distribution on the incident surface onto which daylight is incident (in this embodiment, the window surface) using the acquired first luminance distribution and first contribution matrix.

[0048] Note that in the derivation unit 11B according to the present embodiment, as an example, the light emission intensity distribution is derived using the following formula (2). "View matrix A" in formula (2) is the first contribution matrix, "light vector" is the light emission intensity distribution, and "sensor vector A" is the first luminance distribution. Also, m in formula (2) is the number of pixels (measurement points) in the sensor of the imaging device 30, and p is the number of elements of the light emission intensity distribution of daylight.

[0049]

Equation

[0050] The right side (first luminance distribution) in formula (2) can be obtained by imaging with the imaging device 30. Also, the first contribution matrix on the left side in formula (2) can be obtained by simulation with the prior physical illumination simulation program 13B.

[0051] Thereby, if the number of elements p of the light emission intensity distribution is smaller than the number of measurement points m (if p < m), it becomes theoretically possible to obtain the light emission intensity distribution, which is p unknowns.

[0052] That is, in the luminance distribution estimation device 10 according to the present embodiment, the light emission intensity distribution on the left side in formula (2), that is, the light emission intensity distribution of the light source (window surface) with the highest uncertainty and the greatest influence is set as an unknown, and the photometric quantity (first luminance distribution) of the measurement points where the measurement is relatively easy is acquired by the imaging device 30. Then, in the luminance distribution estimation device 10 according to the present embodiment, the contribution matrix (first contribution matrix) indicating the magnitude of the influence given by the unit light emission intensity to the measurement point is acquired by simulation, and the unknown light emission intensity distribution is derived by a conventionally known method such as the least squares method.

[0053] Here, when discretizing the light intensity distribution on a window surface, it is usually divided into at least 145 parts, as shown in Figure 4, for example. In this case, the number of elements p of the light intensity distribution is 145. However, the larger the number of elements p, the greater the computational load for estimating the light intensity distribution. Figure 4 is a schematic diagram showing an example of a discretized light intensity distribution on a window surface (Source: "Analysis of the performance of prism daylight redirecting systems with bi-directional scattering distribution functions," [online], [searched March 19, 2024], Internet)<URL:https: / / link.springer.com / article / 10.1007 / s12273-020-0607-4> ").

[0054] Therefore, in the luminance distribution estimation device 10 according to this embodiment, the emission intensity distribution is reproduced by a model formula shown in the following formula (3), thereby significantly reducing the number of unknown variables (number of elements p).

[0055]

number

[0056] In equation (3), x and y represent the position of the Cartesian coordinates obtained by converting the spherical coordinates into a two-dimensional plane using the equidistant projection method, L represents the luminance, f represents the anisotropic two-dimensional Gaussian function, and x0 and y0 represent the center coordinates of the Gaussian distribution. x ,σ yrepresents the standard deviation of both axes, a and b represent coefficients that are uniformly multiplied to each Gaussian distribution, and c represents a coefficient that is uniformly added to all brightness components. Here, two-dimensional Gaussian functions are represented by the graphs shown in Figures 5A and 5B, respectively (Source: "Multivariate Normal Distribution," [online], [Retrieved April 8, 2024], Internet)<URL:https: / / ja.wikipedia.org / wiki / %E5%A4%9A%E5%A4%89%E9%87%8F%E6%AD%A3%E8%A6%8F%E5%88%86%E5%B8%83> ").

[0057] The spherical coordinates can be expressed by the following equation, where γ is the altitude angle and θ is the azimuth angle.

[0058] x=γcosθ,y=γsinθ

[0059] In equation (3), the center coordinates are x01, y01, x02, y02, and the standard deviation is σ x 1,σ y 1,σ x 2,σ y 2. The coefficients a, b, and c are unknowns, making the total number of unknowns 11.

[0060] In this way, in the luminance distribution estimation device 10 according to this embodiment, the light source luminance L x,y is replaced by equation (3), which reduces the number of unknowns to be estimated to 11, thereby significantly reducing the computational load compared to conventional techniques.

[0061] The possible patterns of luminance distribution on the window surface are somewhat limited, and are generally limited to the following patterns.

[0062] (a) When direct sunlight and skylight shine simultaneously (b) When only skylight is shining (c) When passing through diffusely transparent materials such as roller blinds and curtains (d) When the light is separated into upward and downward diffused transmitted light, as in the case of blinds

[0063] To reproduce these intensities, it is not necessary to divide the intensities into 145, but they can be reproduced using two two-dimensional Gaussian functions and intercepts. In equation (3), each of the above (a) to (d) can be reproduced in the following state.

[0064] (a) The sum of f1, which has a very small standard deviation, and f2, which has a large standard deviation (b) f1 with large standard deviation (coefficient b of f2 = 0) (c) Same as (b) (d) The sum of any f1 and any f2

[0065] Figure 6 shows an example of the luminance distribution on a window surface according to equation (3). The left image in Figure 6 shows a case where a fully open Venetian blind is installed on the window surface, the middle image shows a case where a roller blind is installed on the window surface, and the right image shows a case where a fully closed Venetian blind is installed on the window surface. The example shown in Figure 6 also illustrates the case where an arbitrary number of divided light source elements are connected and displayed using a gradation.

[0066] On the other hand, the second acquisition unit 11C according to this embodiment acquires a second contribution matrix indicating the magnitude of the influence of daylight on a second measurement point different from the first measurement point. Then, the estimation unit 11D according to this embodiment estimates a second luminance distribution indicating the distribution of luminance at the second measurement point using the emission intensity distribution and the second contribution matrix.

[0067] The estimation unit 11D according to this embodiment estimates the second luminance distribution using, for example, the following equation (4): "view matrix B" in equation (4) is the second contribution matrix, "light vector" is the light emission intensity distribution derived by equation (2), and "sensor vector B" is the second luminance distribution.

[0068]

number

[0069] That is, once the emission intensity distribution is derived by the derivation unit 11B, the photometric quantity (second luminance distribution) of any measurement point can be calculated using the contribution matrix (second contribution matrix) of the measurement point according to formula (4). This makes it possible to estimate the photometric quantity of a measurement point that is difficult to measure on-site (for example, a window surface or ceiling surface from the line of sight of an office worker).

[0070] As described above, the luminance distribution estimation device 10 according to this embodiment estimates the luminance distribution at any measurement point using equations (2) to (4).

[0071] Next, the building-related information database 13C according to this embodiment will be described with reference to Fig. 7. Fig. 7 is a schematic diagram showing an example of the configuration of the building-related information database 13C according to this embodiment. The building-related information database 13C is a database that stores information about buildings whose luminance distributions are to be estimated by the luminance distribution estimation device 10 according to this embodiment.

[0072] As shown in FIG. 7, the building-related information database 13C according to this embodiment stores, in association with each other, information on the building name, building position information, and three-dimensional CAD (Computer Aided Design) information for each building that is the target of processing by the brightness distribution estimation device 10.

[0073] The building name is information indicating the name of the corresponding building, and the building location information is information indicating the construction location of the corresponding building. In this embodiment, the address of the corresponding building is used as the building location information, but this is not limited to this. The building location information may be in a form in which latitude and longitude information is used, or in a form in which altitude is added to the address or latitude and longitude.

[0074] On the other hand, the 3D CAD information is information representing a model (hereinafter referred to as a "building-related model") including building shape information indicating the shape of the corresponding building and reflectance information indicating the light reflectance of the inner surface of each room provided in the building. Note that the building-related model according to this embodiment also includes identification information for identifying each room in the corresponding building.

[0075] In this embodiment, the building-related model is created using predetermined 3D CAD software. In this embodiment, Rhinoceros (registered trademark) is used as the 3D CAD software, but the 3D CAD software is not limited to this. For example, other software such as Revit (registered trademark) may also be used as the 3D CAD software.

[0076] Next, the luminance distribution information database 13D according to this embodiment will be described with reference to Fig. 8. Fig. 8 is a schematic diagram showing an example of the configuration of the luminance distribution information database 13D according to this embodiment. Note that the luminance distribution information database 13D is a database for storing information indicating the luminance distribution estimated by the luminance distribution estimation device 10 according to this embodiment.

[0077] As shown in FIG. 8, the luminance distribution information database 13D according to this embodiment stores, for each building that is the target of the luminance distribution estimation device 10, information on the building name, room name, estimated measurement point position, and luminance distribution in association with each other.

[0078] The building name is the same information as the building name in the building-related information database 13C, the room name is information indicating the name of a room provided in the corresponding building, the estimated measurement point position is information indicating the position to be estimated for the luminance distribution, and the luminance distribution is information indicating the estimated luminance distribution at the position of the corresponding estimated measurement point.

[0079] In this embodiment, the information indicating the estimated measurement point position is information indicating a position in a predetermined three-dimensional coordinate system with a predetermined position in the corresponding building (in this embodiment, the center of gravity position of the building) as the origin, but it goes without saying that this is not limited to this.

[0080] Next, the operation of the luminance distribution estimation device 10 according to this embodiment will be described with reference to Fig. 9 and Fig. 10. When a user inputs an instruction to start execution of a luminance distribution estimation program 13A via the input unit 14, the CPU 11 of the luminance distribution estimation device 10 executes the program 13A, thereby performing the luminance distribution estimation process shown in Fig. 9. Note that, in order to avoid confusion, the following description will be given assuming that a building-related information database 13C has already been constructed and that information on building names and room names has already been registered in a luminance distribution information database 13D.

[0081] In step 100 of FIG. 9, the CPU 11 controls the display unit 15 to display an initial information input screen having a predetermined configuration, and in step 102, the CPU 11 waits until predetermined information is input.

[0082] An example of an initial information input screen according to this embodiment is shown in Fig. 10. As shown in Fig. 10, the initial information input screen according to this embodiment displays a message prompting the user to input information about a room to be processed (hereinafter referred to as "target room"). The initial information input screen according to this embodiment also displays an input area 15A for inputting information about the building in which the target room is located, the target room, the positions of actual measurement points, and the positions of measurement points to be estimated.

[0083] 10 is displayed on the display unit 15, the user inputs the corresponding information into the corresponding input area 15A via the input unit 14, and then presses the end button 15B. In response to this, the determination in step 102 is affirmative, and the process proceeds to step 104. Note that the position of the actual measurement point is the position of the measurement point determined by the imaging device 30, and since this position is a fixed position, the position may be stored in advance.

[0084] In step 104, the CPU 11 reads out 3D CAD information (hereinafter referred to as "building-related information") corresponding to the building input on the initial information input screen from the building-related information database 13C. In step 106, the CPU 11 derives a first contribution matrix at the position of the actual measurement point by the physical lighting simulation program 13B using the building shape information and reflectance information included in the read building-related information.

[0085] In step 108, the CPU 11 acquires the first luminance distribution from the image capturing device 30. In step 110, the CPU 11 applies the derived first contribution matrix and the acquired first luminance distribution to equation (2) to obtain the light source luminance L x,y The emission intensity distribution is derived by applying

[0086] In step 112, the CPU 11 uses the building shape information and reflectance information included in the read building-related information to derive a second contribution matrix at the position of any one of the measurement points to be estimated by the physical lighting simulation program 13B. In step 114, the CPU 11 estimates the second luminance distribution at the measurement point by applying the derived second contribution matrix and the emission intensity distribution obtained by the processing of step 110 to equation (4).

[0087] In step 116, CPU 11 stores (registers) the estimated second luminance distribution in a corresponding storage area of ​​luminance distribution information database 13D. In step 118, CPU 11 determines whether or not derivation and registration of the second luminance distribution has been completed for all measurement points to be estimated that have been input by the user, and if the determination is negative, CPU 11 returns to step 112, whereas if the determination is positive, CPU 11 ends this luminance distribution estimation process.

[0088] The above-described luminance distribution estimation process results in the construction of a luminance distribution information database 13D, as shown in Fig. 8 as an example. The second luminance distribution registered in the luminance distribution information database 13D is used for performing analyses such as calculating the solid angle of an area in the second luminance distribution that is equal to or greater than a predetermined threshold and deriving indices of glare, brightness, etc., and for adjusting the indoor illumination light, the open / close rate of window blinds, etc., using the results of the analysis.

[0089] As described above, according to this embodiment, a first luminance distribution indicating the distribution of luminance obtained by measurement at a predetermined first measurement point and a first contribution matrix indicating the magnitude of the influence of daylight on the first measurement point are obtained, and the luminance intensity distribution on the incident surface onto which daylight is incident is derived as a sum of multiple two-dimensional Gaussian functions using the first luminance distribution and the first contribution matrix. Furthermore, according to this embodiment, a second contribution matrix indicating the magnitude of the influence of daylight on a second measurement point different from the first measurement point is obtained, and the luminance intensity distribution and the second contribution matrix are used to estimate a second luminance distribution indicating the distribution of luminance at the second measurement point. This eliminates the need to use a visible image at the second measurement point, making it possible to estimate the luminance distribution at a measurement point different from the actual measurement point without being affected by blind spots during imaging.

[0090] Furthermore, according to this embodiment, two two-dimensional Gaussian functions are applied as the plurality of two-dimensional Gaussian functions, which makes it possible to estimate the luminance distribution more easily than when three or more two-dimensional Gaussian functions are used.

[0091] In the above embodiment, the case where illumination light from artificial lighting installed in the room for which the luminance distribution is to be estimated is not taken into consideration is described. However, the present invention is not limited to this, and the illumination light from the artificial lighting may also be taken into consideration.

[0092] In this embodiment, the first acquisition unit 11A further acquires a third contribution matrix indicating the magnitude of the influence on the first measurement point when the dimming rate of illumination light from artificial lighting is 100%, and the dimming rate. Also in this embodiment, the derivation unit 11B derives the emission intensity distribution using the first luminance distribution, the first contribution matrix, the third contribution matrix, and the dimming rate.

[0093] In this embodiment, the second acquisition unit 11C further acquires a fourth contribution matrix indicating the magnitude of the influence of the illumination light on the second measurement point, and the estimation unit 11D estimates the second luminance distribution using the emission intensity distribution, the second contribution matrix, the fourth contribution matrix, and the dimming rate. This embodiment will be specifically described below.

[0094] When the luminance distribution estimation device 10 according to this embodiment is used in an actual building, the photometric quantity measured by the image capture device 30 may include components due to artificial lighting as well as light sources from windows. In this case, the illuminance due to artificial lighting can be subtracted.

[0095] The formula for this case is shown below. Note that the subscripts A and B in the formula below represent w for window, a for artificial lighting, and t for the sum of window and artificial lighting. Also, in the formula below, q is the number of elements in the luminous intensity distribution of artificial lighting.

[0096] The second term on the left side of the following equation (5) calculates the illuminance due to artificial lighting. The contribution matrix aA corresponds to the third contribution matrix described above and is the illuminance provided at the measurement point by one artificial light or one control zone when the dimming rate is 100%. This contribution matrix aA can also be calculated by simulation using the physical lighting simulation program 13B or the like, but it can also be calculated from measurements taken by the image capture device 30 at night when there is no daylight.

[0097] The dimming rate of each artificial light or each control zone can be obtained from monitoring data from a central monitoring facility, etc. Therefore, the illuminance of the artificial light can be obtained by multiplying the contribution matrix aA by the vector of the dimming rate of the artificial light.

[0098]

number

[0099] Furthermore, after the luminous intensity distribution of the window is obtained by applying equation (3) to equation (5), in the phase of calculating the photometric quantity at an arbitrary measurement point, the photometric quantity at the arbitrary measurement point, i.e., the second luminance distribution, can be derived (estimated) by adding the illuminance of artificial lighting as shown in the following equation (6). However, the contribution matrix aB in this case, i.e., the fourth contribution matrix, can be obtained by simulation using the physical lighting simulation program 13B or the like, but cannot be obtained from the measured values ​​obtained by the image capturing device 30.

[0100]

number

[0101] In the above embodiment, a case where a plurality of two-dimensional Gaussian functions are applied as the plurality of functions of the technology of the present disclosure has been described, but the present disclosure is not limited to this. For example, a plurality of functions that, when graphed, form a curved surface with an upward convex shape and with varying likelihood, such as a two-dimensional Laplace distribution function or a spherical Gaussian function, may be applied as the plurality of functions of the technology of the present disclosure.

[0102] In the above embodiment, a case where an anisotropic two-dimensional Gaussian function is applied as the two-dimensional Gaussian function of the technology of the present disclosure has been described, but the present disclosure is not limited to this. For example, an isotropic two-dimensional Gaussian function may be applied as the two-dimensional Gaussian function of the technology of the present disclosure.

[0103] Furthermore, the configurations of the various databases applied in the above embodiment are merely examples, and it goes without saying that the present invention is not limited to the examples.

[0104] Furthermore, in the above embodiment, for example, the following various processors can be used as the hardware structure of the processing units that execute the processes of the first acquisition unit 11A, the derivation unit 11B, the second acquisition unit 11C, and the estimation unit 11D. As described above, the various processors include a CPU, which is a general-purpose processor that executes software (programs) and functions as a processing unit, as well as dedicated electrical circuits that are processors having a circuit configuration specifically designed to execute specific processes, such as a programmable logic device (PLD) that is a processor whose circuit configuration can be changed after manufacture, such as an FPGA (Field-Programmable Gate Array), and an ASIC (Application Specific Integrated Circuit).

[0105] The processing unit may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA).The processing unit may also be configured with a single processor.

[0106] Examples of configuring a processing unit with a single processor include, first, a form in which one processor is configured with a combination of one or more CPUs and software, and this processor functions as the processing unit, as typified by computers such as client and server. Second, a form in which a processor is used to realize the functions of the entire system, including the processing unit, on a single IC (Integrated Circuit) chip, as typified by systems on chips (SoCs). In this way, the processing unit is configured using one or more of the above-mentioned various processors as a hardware structure.

[0107] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. [Explanation of symbols]

[0108] 10. Luminance distribution estimation device 11 CPU 11A 1st acquisition part 11B Derivation part 11C 2nd Acquisition Part 11D Estimation Section 12 Memory 13 Storage section 13A Luminance distribution estimation program 13B Physical Lighting Simulation Program 13C Building-related information database 13D luminance distribution information database 14 Input section 15 Display section 15A input range 15B Exit button 16 Media reading and writing device 17 Recording Media 18 Communication I / F section 30 Imaging equipment

Claims

1. a first acquisition unit that acquires a first luminance distribution that indicates a distribution of luminance obtained by measurement at a predetermined first measurement point, and a first contribution matrix that indicates the magnitude of the influence of daylight on the first measurement point; a derivation unit that uses the first luminance distribution and the first contribution matrix to derive a luminance intensity distribution on the incident surface onto which the daylight is incident, the luminance intensity distribution being reproduced as a sum of a plurality of functions that, when graphed, form an upwardly convex surface and whose likelihood varies; a second acquisition unit that acquires a second contribution matrix indicating the magnitude of the influence of the daylight on a second measurement point different from the first measurement point; an estimation unit that estimates a second luminance distribution indicating a distribution of luminance at the second measurement points using the emission intensity distribution and the second contribution matrix; A luminance distribution estimation device comprising:

2. the plurality of functions are a plurality of two-dimensional Gaussian functions; The luminance distribution estimation device according to claim 1 .

3. the plurality of two-dimensional Gaussian functions are two two-dimensional Gaussian functions; The luminance distribution estimation device according to claim 2 .

4. the first acquisition unit further acquires a third contribution matrix indicating a magnitude of an influence on the first measurement point when a dimming rate of illumination light by artificial lighting is 100%, and the dimming rate; the derivation unit derives the emission intensity distribution using the first luminance distribution, the first contribution matrix, the third contribution matrix, and the dimming rate; the second acquisition unit further acquires a fourth contribution matrix indicating a magnitude of an influence of the illumination light on the second measurement point; the estimation unit estimates the second luminance distribution using the emission intensity distribution, the second contribution matrix, the fourth contribution matrix, and the dimming rate. The luminance distribution estimation device according to claim 1 or 2.

5. A first luminance distribution indicating a distribution of luminance obtained by measurement at a predetermined first measurement point and a first contribution matrix indicating the magnitude of the influence of daylight on the first measurement point are obtained; using the first luminance distribution and the first contribution matrix, deriving a luminance intensity distribution on the incident surface onto which the daylight is incident as a distribution that is reproduced by the sum of a plurality of functions that, when graphed, form an upwardly convex shape and that form a curved surface with varying likelihood; obtaining a second contribution matrix indicating the magnitude of the influence of the daylight on a second measurement point different from the first measurement point; estimating a second luminance distribution indicating a distribution of luminance at the second measurement points using the emission intensity distribution and the second contribution matrix; A brightness distribution estimation program that causes a computer to execute the processing.

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