Probabilistic Directed Distance Fields for 3D Shape Representation
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Solution Overview
Problem
Existing shape representations in differentiable rendering, such as explicit and implicit representations, face challenges in providing optimal geometric fidelity and efficiency for various tasks like deformation, segmentation, and correspondence.
Innovation Solution
The use of probabilistic directed distance fields (PDDFs) to represent shapes, which involves obtaining a camera representation and a latent shape vector, calculating visibility scores and depths for rays emanating from determined positions and directions, and generating renders of geometric information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If explicit shape representations (voxels, point clouds, meshes) are used, then rendering is relatively easy, but geometric fidelity is limited and topological constraints apply
Solution Approach 1:
The patent introduces an intermediary representation called directed distance field (DDF) that acts as a bridge between explicit and implicit shape representations. The DDF uses directed distances from query points to shape surfaces along specific directions, providing both the ease of explicit representations and the geometric fidelity of implicit representations, thereby resolving the contradiction between rendering ease and geometric fidelity
Solution Approach 2:
The patent transforms the shape representation from traditional spatial coordinates to directed distance parameters. By representing shapes through directed distances along rays rather than through voxel grids, point clouds, or mesh surfaces, the system achieves higher geometric fidelity while maintaining rendering efficiency through parameter-based queries
2Manufacturing precision
If implicit shape representations (occupancy, distance, or radiance fields) are used, then geometric fidelity is preserved, but rendering processes become complex and inefficient
Solution Approach 1:
The patent segments the implicit representation into directional components by introducing directed distances along specific rays. Instead of evaluating complex implicit functions at every pixel, the system segments the problem into ray-based queries where directed distances are computed independently for each ray, significantly improving rendering efficiency while preserving geometric fidelity
Solution Approach 2:
The patent replaces the traditional mechanical rendering process of implicit representations (which requires multiple network forward passes and complex calculations per pixel) with a substituted approach using directed distance fields. This substitution allows for more efficient computation by directly querying directed distances along rays without requiring complex iterative solving processes
3Ease of manufacture
If traditional shape representations are used for tasks like deformation, segmentation, and correspondence, then implementation is straightforward, but scalability and efficiency are limited
Solution Approach 1:
The patent creates a universal directed distance field representation that can be applied to multiple tasks including deformation, segmentation, and correspondence. The DDF framework provides a unified approach that works across different tasks by representing shapes through directed distances, enabling efficient and scalable implementation without requiring task-specific representations, thereby improving both versatility and efficiency
Data Source
AI summary
The present disclosure provides methods, apparatuses, and computer-readable mediums for representing shapes with probabilistic directed distance fields. In some embodiments, a method includes obtaining a camera representation and a latent shape vector representation of a scene. The camera representation indicates position information and direction information of a view of the scene. The method further includes calculating, based on the latent shape vector representation of the scene, a visibility score and a depth for each ray of a plurality of rays emanating from a corresponding plurality of positions and directions. The plurality of positions and directions are determined from the camera representation of the scene. The method further includes generating renders of geometric information of the scene using the visibility score and the depth of the plurality of rays.