Abstract: The disclosure provides implicit representations for multi-object 3D shape, 6D pose and size, and appearance optimization, including obtaining shape, 6D pose and size, and appearance codes. Training is employed using shape and appearance priors from an implicit joint differential database. 2D masks are also obtained and are used in an optimization process that utilizes a combined loss minimizing function and an Octree-based coarse-to-fine differentiable optimization to jointly optimize the latest shape, appearance, pose and size, and 2D masks. An object surface is recovered from the latest shape codes to a desired resolution level. The database represents shapes as Signed Distance Fields (SDF), and appearance as Texture Fields (TF).

Abstract: System, methods, and other embodiments described herein relate to single-shot multi-object three-dimensional (3D) shape reconstruction and categorical six-dimensional (6D) pose and size estimation. In one embodiment, a method includes inferring a heatmap based upon a feature pyramid, where the feature pyramid is generated based upon a red green blue depth (RGB-D) image that includes objects. The method further includes sampling a 3D parameter map at locations corresponding to peaks in the heatmap, where the 3D parameter map is inferred based upon the feature pyramid, and where the locations include latent shape codes, 6D poses, and one-dimensional (1D) scales. The method further includes generating point clouds based upon the latent shape codes, the 6D poses, and the 1D scales.

Abstract: A method, apparatus and system for efficient navigation in a navigation space includes determining semantic features and respective 3D positional information of the semantic features for scenes of captured image content and depth-related content in the navigation space, combining information of the determined semantic features of the scene with respective 3D positional information using neural networks to determine an intermediate representation of the scene which provides information regarding positions of the semantic features in the scene and spatial relationships among the sematic features, and using the information regarding the positions of the semantic features and the spatial relationships among the sematic features in a machine learning process to provide at least one of a navigation path in the navigation space, a model of the navigation space, and an explanation of a navigation action by the single, mobile agent in the navigation space.