23 results about "Map decoding" patented technology

The invention provides a satellite image and actually measured RSRP fused ground radio map generation system, and the system comprises a satellite image feature extraction module which is used for converting a high-resolution satellite image of a target region into high-dimensional image features reflecting the ground feature type distribution of the region; the base station physical characteristic input module is used for converting the physical parameters of the communication base station in the target area into characteristic vectors reflecting electromagnetic propagation characteristics; the multi-modal fusion module is used for fusing the high-dimensional image features and the feature vectors to generate joint feature representation; the radio map decoder is used for generating a radio signal power plane distribution diagram of the target area through a decoding network based on the joint feature representation; and the ground RSRP correction module is used for correcting the radio signal power plane distribution diagram output by the radio map decoder so as to output a final radio signal power distribution result. According to the invention, the space-ground collaborative data source fusion is realized to improve the mapping precision.

The invention provides an online map construction method and device fusing time sequence prior and historical global map prior, and relates to the technical field of online map construction.The method comprises the steps that time sequence BEV features and online generated BEV features are efficiently fused through multi-channel cross attention based on masks; adaptively fusing the time sequence enhanced BEV features and local BEV features provided by a historical map through a spatial prior gating network; a mixed prior ROI mask is generated through prior information provided by a time sequence and a historical global map, and initialization of map query in map decoding at the current moment is assisted, so that the reliability of online map construction is improved.

The invention provides a two-dimensional radio map generation system fusing communication base station and environment information, and the system comprises a base station encoder which is used for converting physical parameters of a communication base station in a target region into feature vectors reflecting electromagnetic rebroadcasting features; the environment encoder is used for converting the map of the target area containing the environment information into environment image features reflecting the global features of the target area; the environment image features and the feature vectors are fused to generate joint feature representation; and the radio map decoder is used for generating a radio signal power plane distribution diagram of the target area through a decoding network based on the joint feature representation. The accuracy of the two-dimensional radio map is effectively improved.

The invention belongs to the technical field of map construction, and discloses an online vectorization map construction method based on dynamic query management. Extracting perspective image features; generating aerial view view angle features based on the perspective image features; initializing map element query; inputting the initialized map element query and the aerial view view angle features into a map decoder based on dynamic query management, and optimizing the map element query; taking the output result of the Lth layer of the map decoder based on the dynamic query management as the final prediction of the attribute of the map element; and carrying out loss calculation and training. By optimizing and dynamically managing map queries in a map decoder, the computational consumption of low confidence map queries is effectively reduced. Under the same computing resources, the model is allowed to process a larger number of map queries, so that the construction precision of the vectorized map is improved. The research of automatic driving on-line map construction is further promoted, and the deployment of an on-line vectorization map model is facilitated.

Systems and methods for error detection in quantum computing systems are provided. In one example, the method includes obtaining a multi-dimensional quantum error detection map. The multi-dimensional quantum error detection map represents one or more quantum error detection measurements over a period of time. The method includes determining a partitioning scheme and a fusion scheme for the multi-dimensional quantum error detection map based at least in part on the decoding delay and the fusion delay. The method includes partitioning the multi-dimensional quantum error detection map into a plurality of blocks based at least in part on the partitioning scheme. The method includes decoding each of the plurality of blocks. The method includes fusing the plurality of blocks into a decoded detection map based at least in part on the fusion scheme. The method includes operating a quantum computing system based at least in part on the decoded detection map.

To provide a device for efficiently encoding / decoding a feature map with a small processing amount.SOLUTION: The feature map decoding device 200 includes a feature map internal decoding unit for decoding the first single-scale feature maps that have been packed into frames and encoded to generate integer-type packed feature frames, and a feature map internal decoding unit for converting elements of the integer-type packed feature frames into decimal values to generate decimal-type packed feature frames. A feature map inverse transformation unit for generating a second single-scale feature map by dividing the second single-scale feature map into one or more packing groups based on information about the packing groups and unpacking the second single-scale feature map, a single-scale feature map refinement unit for generating a third single-scale feature map by calculating a distribution characteristic of the second single-scale feature map and refining the second single-scale feature map, and a feature map restoration unit for generating a multi-scale feature map by transforming the third single-scale feature map.SELECTED DRAWING: Figure 2

This application discloses a land use change prediction method, apparatus, device, and medium based on map representation generation learning, relating to the fields of spatiotemporal big data mining and urban planning technology. The method includes: constructing multi-source map submaps and performing binarization and attention weighting to obtain binarized land use submaps and weighted geographic environment submap features; extracting deep aggregated spatial features from both to generate latent vector representations; flattening the representation into a one-dimensional feature sequence and querying the index of each element in a landscape codebook to form a vectorized index sequence; combining relative distance encoding, using a map sequence generation learning model to predict future vectorized index sequences, and querying the landscape codebook to obtain future vectorized tensors; iteratively decoding the tensor using a map decoder to generate land use change results. This application achieves more accurate land use change prediction by capturing mesoscale spatial local information and reducing unit-level error accumulation.

The application provides a vector map construction method and system based on prior map and multi-stage diffusion reasoning. A unified vector encoder is used to encode multiple prior maps to generate prior features. Real-time data obtained is converted into bird's-eye view features, and multi-stage diffusion reasoning is performed to map the bird's-eye view features of the degradation domain to diffusion features of the normal domain. The prior features and the diffusion features are fused to obtain fused features. The fused features are input into a constructed map decoder to generate an online vector map. In the scheme, multiple prior maps are introduced, and a unified vector encoder is used to efficiently encode different prior maps, thereby enhancing the robustness and accuracy of online map construction. Through multi-stage diffusion reasoning technology, image details are gradually optimized, which not only uniformly processes multiple image degradation problems, but also provides high-quality image restoration and enhancement effect in complex environments, thereby providing strong support for an automatic driving perception system.

A feature map encoding device (100) determines, from feature maps of a plurality of channels, an active channel to be encoded, generates one packing feature frame by packing only the feature map of the active channel, and generates a bit stream by encoding the packing feature frame.

A multi-layer feature map encoding method and device of the present disclosure comprises the steps of: acquiring a reduced feature map by reducing a multi-layer feature map extracted from an input image; and encoding the reduced feature map into a bitstream, wherein the reduction of the multi-layer feature map can be performed using a multi-layer feature map reduction means including a plurality of multi-layer feature map reduction blocks. In addition, the multi-layer feature map decoding method and device of the disclosure may comprise the steps of: acquiring a reduced feature map by decoding a bitstream; and restoring a multi-layer feature map by using the reduced feature map as an input of a multi-layer feature map restoration means including a plurality of multi-layer feature map restoration blocks.

This application discloses a gain map encoding method and apparatus, a gain map decoding method and apparatus, a device, and a medium. The method of this application includes: determining a first picture region and a second picture region in a gain map, where a brightness of a pixel point in the first picture region is greater than a first threshold, and a brightness of a pixel point in the second picture region is less than or equal to the first threshold; determining an encoding strategy of the gain map based on the first picture region and the second picture region; and encoding the gain map based on the encoding strategy, to obtain encoded information of the gain map.

To provide a device for efficiently encoding / decoding a feature map with a small processing amount.SOLUTION: In a feature map encoding device including a feature map reducing section, a feature map converting section, and a feature map internal encoding section, the feature map converting section 103 includes a range adjusting section that adjusts, for feature maps of a plurality of channels supplied from the feature map reducing section, feature amounts included in the feature maps on the basis of a parameter including a scaling coefficient and an offset coefficient, and a packing section that combines the feature maps of the plurality of channels into one frame to generate a packed feature frame, and the range adjusting section selects, for each element included in the parameter, whether encoding is performed in units of frames or in units of channels.SELECTED DRAWING: Figure 15

The invention relates to a map generation method, a map generation device and a computer program product. The map generation method comprises the following steps: carrying out feature coding on vehicle environment information to obtain current aerial view features; obtaining an absolute position of the current aerial view feature according to the current self-parking space posture, and downloading a prior aerial view feature from a server according to the current aerial view feature and the absolute position; performing position correction on the current aerial view feature according to the prior aerial view feature to obtain a corrected current aerial view feature; fusing the corrected current aerial view feature and the prior aerial view feature to obtain a local aerial view feature; obtaining a plurality of map queries, and inputting the plurality of map queries and the local aerial view features into a pre-trained map decoder for processing to obtain a decoding result; and generating a plurality of map elements according to the decoding result, and generating a local map according to the plurality of map elements. According to the invention, the construction performance of the on-line vectorization high-precision map realized based on the vehicle can be improved.

This application discloses a gain map encoding method and apparatus, a gain map decoding method and apparatus, a device, and a medium, and pertains to the field of communication technologies. The method in this application includes: obtaining at least one target image block in a gain map, where a luminance value of a pixel in the target image block is greater than a first threshold (301); determining target image block identification information of the gain map and an image block group based on the at least one target image block (302); and encoding the target image block identification information and the image block group to obtain encoding information of the gain map (303).

This provides technology for encoding and decoding feature maps. [Solution] The present invention provides a feature map encoding device comprising: a feature map reduction model registration unit that registers a feature map reduction model and a corresponding feature map restoration model at the position indicated by a first model index in a feature map reduction model storage unit; a feature map reduction model selection unit that determines a second model index; a feature map reduction unit that generates a single-scale feature map using the feature map reduction model identified by the second model index; and a feature map restoration parameter encoding unit that transmits information indicating the first model index, the second model index, and the feature map restoration model identified by the second model index.

A feature map encoding device according to one embodiment of the present application comprises: a neural network feature amount extraction unit (101) that generates a multi-scale feature map from an image; a feature map reduction unit (102) that converts the multi-scale feature map into a single-scale feature map; a feature map conversion unit (103) that converts the single-scale feature map into a packing feature frame; a distribution characteristic calculation unit (111) that calculates distribution characteristics of the multi-scale feature map and distribution characteristics of the single-scale feature map on a frame-by-frame basis; and a feature map internal encoding unit (104) that encodes the packing feature frame and the distribution characteristics.

The invention relates to a multi-scale high-precision map construction method based on double-space interaction, and belongs to the technical field of high-precision map construction. According to the method, a double-space feature modeling and interaction mechanism is introduced. The method comprises the following steps: firstly, performing feature extraction on multi-view image data, projecting the multi-view image data to a bird's-eye view space, and generating multi-scale BEV feature representation; and then, carrying out scale-by-scale fusion on the multi-scale BEV features of the current frame and the historical prior BEV features in an Euclidean space so as to enhance the continuity and stability of a map structure. On the basis, the fused multi-scale BEV features are mapped to a hyperbolic space, modeling is carried out on the hierarchical relationship among different scale features by using the geometric characteristics of the hyperbolic space, and cross-scale feature interaction is realized. And finally, the advantages of the Euclidean space features and the hyperbolic space features are integrated through a double-space feature fusion mode, BEV feature representation used for high-precision map decoding is generated, and therefore the overall precision of map construction is improved.

This provides technology for encoding and decoding feature maps. [Solution] The present invention provides a feature map encoding device comprising: a feature map reduction unit that converts a multi-scale feature map to generate a single-scale feature map; a packing unit that identifies the scan type of the single-scale feature map, packs the single-scale feature map into a frame based on the identified scan type to generate a packed feature frame; an encoding unit that encodes the scan type; and a feature map internal encoding unit that encodes the packed feature frame.