Abstract: A text-video recommendation model determines relevance of a text to a video in a text-video pair (e.g., as a relevance score) with a text embedding and a text-conditioned video embedding. The text-conditioned video embedding is a representation of the video used for evaluating the relevance of the video to the text, where the representation itself is a function of the text it is evaluated for. As such, the input text may be used to weigh or attend to different frames of the video in determining the text-conditioned video embedding. The representation of the video may thus differ for different input texts for comparison. The text-conditioned video embedding may be determined in various ways, such as with a set of the most-similar frames to the input text (the top-k frames) or may be based on an attention function based on query, key, and value projections.

Abstract: A system, device and method of generating high resolution and high accuracy point cloud. In one aspect, a computer vision system receives a camera point cloud from a camera system and a LiDAR point cloud from a LiDAR system. An error of the camera point cloud is determined using the LiDAR point cloud as a reference. A correction function is determined based on the determined error. A corrected point cloud is generated from the camera point cloud using the correction function. A training error of the corrected point cloud is determined using the first LiDAR point cloud as a reference. The correction function is updated based on the determined training error. When training is completed, the correction function can be used by the computer vision system to generate a generating high resolution and high accuracy point cloud from the camera point cloud provided by the camera system.

Abstract: A method and system for generating a semantic point cloud map. Voice input is received via a microphone and converted into text via speech-to-text synthesis. The text is decomposed into semantic data comprising a number of words, and it is determined whether keywords of the semantic data are present in an autonomous driving (AD) ontology. In response to the keywords of the semantic data being present in the AD ontology, coordinates of a point cloud map corresponding to the semantic data are determined. An association between a semantic label determined from the words of the semantic data and coordinates in the point cloud map corresponding to the semantic data is generated and stored in a memory of the computer vision system.

Abstract: A system, device and method of generating high resolution and high accuracy point cloud. In one aspect, a computer vision system receives a camera point cloud from a camera system and a LiDAR point cloud from a LiDAR system. An error of the camera point cloud is determined using the LiDAR point cloud as a reference. A correction function is determined based on the determined error. A corrected point cloud is generated from the camera point cloud using the correction function. A training error of the corrected point cloud is determined using the first LiDAR point cloud as a reference. The correction function is updated based on the determined training error. When training is completed, the correction function can be used by the computer vision system to generate a generating high resolution and high accuracy point cloud from the camera point cloud provided by the camera system.

Abstract: A group optimization method for constructing a 3D feature map is disclosed. In one embodiment, the method comprises determining correspondence information for a plurality of environmental features for each image in a group of images in which a respective environmental feature is present and relative position and alignment of each camera. Depth information is determined for each environmental feature in the plurality of environmental features for each image in the group of images in which a respective environmental feature is present based on the correspondence information of the environmental features. Group optimized depth information is determined for each environmental feature in the plurality of environmental features using the determined depth information of each respective environmental feature.

Abstract: A method and system for generating a semantic point cloud map. Voice input is received via a microphone and converted into text via speech-to-text synthesis. The text is decomposed into semantic data comprising a number of words, and it is determined whether keywords of the semantic data are present in an autonomous driving (AD) ontology. In response to the keywords of the semantic data being present in the AD ontology, coordinates of a point cloud map corresponding to the semantic data are determined. An association between a semantic label determined from the words of the semantic data and coordinates in the point cloud map corresponding to the semantic data is generated and stored in a memory of the computer vision system.

Abstract: A group optimization method for constructing a 3D feature map is disclosed. In one embodiment, the method comprises determining correspondence information for a plurality of environmental features for each image in a group of images in which a respective environmental feature is present and relative position and alignment of each camera. Depth information is determined for each environmental feature in the plurality of environmental features for each image in the group of images in which a respective environmental feature is present based on the correspondence information of the environmental features. Group optimized depth information is determined for each environmental feature in the plurality of environmental features using the determined depth information of each respective environmental feature.