Abstract: Techniques for decimating portions of a map of an environment are discussed herein. The environment can be represented by a three-dimensional (3D) map including a plurality of polygons and semantic information associated with the polygons. In some cases, decimation operations may be based on semantic information associated with the environment. Differing decimation operations and/or levels may be applied to polygons of different semantic classifications or differing contribution levels. Boundaries between regions having different semantic information can be preserved. Meshes can be decimated using different decimation operators or decimation levels and an accuracy of localizing can be compared using the various decimated meshes. An optimal mesh can be selected and sent to vehicles for localizing the vehicles in the environment.

Abstract: Techniques for decimating portions of a map of an environment are discussed herein. The environment can be represented by a three-dimensional (3D) map including a plurality of polygons and semantic information associated with the polygons. In some cases, decimation operations may be based on semantic information associated with the environment. Differing decimation operations and/or levels may be applied to polygons of different semantic classifications or differing contribution levels. Boundaries between regions having different semantic information can be preserved. Meshes can be decimated using different decimation operators or decimation levels and an accuracy of localizing can be compared using the various decimated meshes. An optimal mesh can be selected and sent to vehicles for localizing the vehicles in the environment.

Abstract: Techniques for generating maps without shadows are discussed herein. A plurality of images can be captured by a vehicle traversing an environment representing various perspectives and/or lighting conditions in the environment. A shadow within an image can be identified by a machine learning algorithm trained to detect shadows in images and/or by projecting the image onto a three-dimensional (3D) map of the environment and identifying candidate shadow regions based on the geometry of the 3D map and the location of the light source. Shadows can be removed or minimized by utilizing blending or duplicating techniques. Color information and reflectance information can be added to the 3D map to generate a textured 3D map. A textured 3D map without shadows can be used to simulate the environment under different lighting conditions.

Abstract: Techniques for generating maps without shadows are discussed herein. A plurality of images can be captured by a vehicle traversing an environment representing various perspectives and/or lighting conditions in the environment. A shadow within an image can be identified by a machine learning algorithm trained to detect shadows in images and/or by projecting the image onto a three-dimensional (3D) map of the environment and identifying candidate shadow regions based on the geometry of the 3D map and the location of the light source. Shadows can be removed or minimized by utilizing blending or duplicating techniques. Color information and reflectance information can be added to the 3D map to generate a textured 3D map. A textured 3D map without shadows can be used to simulate the environment under different lighting conditions.

Abstract: Techniques for generating maps without shadows are discussed herein. A plurality of images can be captured by a vehicle traversing an environment representing various perspectives and/or lighting conditions in the environment. A shadow within an image can be identified by a machine learning algorithm trained to detect shadows in images and/or by projecting the image onto a three-dimensional (3D) map of the environment and identifying candidate shadow regions based on the geometry of the 3D map and the location of the light source. Shadows can be removed or minimized by utilizing blending or duplicating techniques. Color information and reflectance information can be added to the 3D map to generate a textured 3D map. A textured 3D map without shadows can be used to simulate the environment under different lighting conditions.

Abstract: Techniques for generating maps without shadows are discussed herein. A plurality of images can be captured by a vehicle traversing an environment representing various perspectives and/or lighting conditions in the environment. A shadow within an image can be identified by a machine learning algorithm trained to detect shadows in images and/or by projecting the image onto a three-dimensional (3D) map of the environment and identifying candidate shadow regions based on the geometry of the 3D map and the location of the light source. Shadows can be removed or minimized by utilizing blending or duplicating techniques. Color information and reflectance information can be added to the 3D map to generate a textured 3D map. A textured 3D map without shadows can be used to simulate the environment under different lighting conditions.

Abstract: Techniques for decimating portions of a map of an environment are discussed herein. The environment can be represented by a three-dimensional (3D) map including a plurality of polygons and semantic information associated with the polygons. In some cases, decimation operations may be based on semantic information associated with the environment. Differing decimation operations and/or levels may be applied to polygons of different semantic classifications or differing contribution levels. Boundaries between regions having different semantic information can be preserved. Meshes can be decimated using different decimation operators or decimation levels and an accuracy of localizing can be compared using the various decimated meshes. An optimal mesh can be selected and sent to vehicles for localizing the vehicles in the environment.

Abstract: Techniques for decimating portions of a map of an environment are discussed herein. The environment can be represented by a three-dimensional (3D) map including a plurality of polygons and semantic information associated with the polygons. In some cases, decimation operations may be based on semantic information associated with the environment. Differing decimation operations and/or levels may be applied to polygons of different semantic classifications or differing contribution levels. Boundaries between regions having different semantic information can be preserved. Meshes can be decimated using different decimation operators or decimation levels and an accuracy of localizing can be compared using the various decimated meshes. An optimal mesh can be selected and sent to vehicles for localizing the vehicles in the environment.