Posed Signed Distance Fields for Real-Time Deformable Contact
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Solution Overview
Problem
Conventional contact simulations in virtual reality environments suffer from computational inefficiencies due to the high complexity of triangle meshes and dual conversion processes, particularly when objects collide, leading to inefficient representation and reconstruction of kinematics.
Innovation Solution
The method involves computing a kinematic descriptor for deformable objects using a lower dimensional description, parameterized by a posed signed distance field through a function that regresses the field, utilizing model reduction techniques and neural networks to enhance computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If triangle meshes are used to represent colliding objects in contact simulations, then the representation accuracy is improved, but the computational complexity increases significantly (O(N) complexity for N triangles)
Solution Approach 1:
The patent transforms the high-dimensional triangle mesh representation into a low-dimensional latent space representation using an autoencoder. The encoder compresses the 3D mesh into a compact latent code, and the decoder reconstructs the mesh from this code. This dimensional transformation reduces computational complexity while maintaining representation accuracy, as the latent space captures the essential geometric information with far fewer dimensions than the original mesh.
Solution Approach 2:
The patent creates a learned representation (latent code) that copies the essential information from the original triangle mesh. Instead of working with the full mesh directly, the system uses this compressed copy in the latent space for contact simulation computations, then reconstructs only when needed. This copying approach allows efficient computation while preserving the necessary geometric details.
2Productivity
If model reduction is used to represent kinematics in lower dimensionality, then computational efficiency is improved, but the accuracy of collision detection may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the low-dimensional latent representation is continuously refined through interaction with the environment. During contact simulation, the latent code is updated based on collision detection results and physical constraints, allowing the simplified representation to adapt and maintain accuracy. This feedback loop ensures that the reduced-dimensional model remains sufficiently accurate for reliable collision detection while benefiting from computational efficiency.
Solution Approach 2:
The patent changes the representation parameters from explicit triangle mesh coordinates to latent space codes. This parameter transformation allows the system to work with a simplified representation during simulation, improving computational efficiency. The latent parameters are designed to capture the essential kinematic information needed for accurate collision detection, bridging the gap between simplicity and accuracy.
3Measurement precision
If dual conversion process is used (reconstructing outside surfaces in full dimensionality during contact), then collision query accuracy is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-training the autoencoder model to efficiently encode and decode mesh representations. The encoder is trained to create compact latent codes from 3D meshes, and the decoder is trained to reconstruct accurate surface geometries from these codes. This pre-training enables fast inference during actual contact simulation, allowing the system to quickly convert between representations only when needed, rather than continuously maintaining full-dimensional representations.
Data Source
AI summary
A method processes contact in physics simulations of multi-bodies. The method includes computing a kinematic descriptor for a deformable object based on a lower dimensional description. The method also includes learning a posed signed distance field parameterized by the kinematic descriptor using a function that regresses the field. The method also includes performing contact simulation based on the posed signed distance field for the deformable object.