SparseDR: Differentiable Rendering of Sparse Signed Distance Fields

SparseDR: Differentiable Rendering of Sparse Signed Distance Fields

Alexey Budak, Albert Garifullin, Vladimir Frolov

Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence
Demo Track. Pages 8371-8374. https://doi.org/10.24963/ijcai.2026/959

We present SparseDR, a novel differentiable rendering algorithm designed for sparse representations based on Signed Distance Fields (SDF). We leverage the Sparse Brick Set representation and propose an adaptation of redistancing for sparse SDFs. This enables SparseDR to surpass existing works in accuracy of surface reconstruction by increasing the effective resolution of the SDF representation. SparseDR is efficiently implemented in C++ and Vulkan, achieving several times shorter reconstruction time than other methods.
Keywords:
AI: Computer Vision
AI: Machine Learning