OGPP is a particle flow-matching method using orbit-space canonicalization and geometric paths that achieves lower error and fewer steps than prior approaches on 3D benchmarks.
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SAD is a new explicit differentiable image representation based on soft anisotropic additively weighted Voronoi partitions that achieves higher PSNR and 4-19x faster training than Image-GS and Instant-NGP at matched bitrate.
A new GPU clipping algorithm with directional culling and hierarchical traversal constructs scalable 3D Voronoi and power diagrams for arbitrary point distributions.
citing papers explorer
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Generative Modeling with Orbit-Space Particle Flow Matching
OGPP is a particle flow-matching method using orbit-space canonicalization and geometric paths that achieves lower error and fewer steps than prior approaches on 3D benchmarks.
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Soft Anisotropic Diagrams for Differentiable Image Representation
SAD is a new explicit differentiable image representation based on soft anisotropic additively weighted Voronoi partitions that achieves higher PSNR and 4-19x faster training than Image-GS and Instant-NGP at matched bitrate.
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Scalable GPU Construction of 3D Voronoi and Power Diagrams
A new GPU clipping algorithm with directional culling and hierarchical traversal constructs scalable 3D Voronoi and power diagrams for arbitrary point distributions.