arXiv Computer Vision
CVTSplat: Multi-view Reconstruction Using an Adaptive Centroidal Voronoi Tessellation
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CVTSplat: Multi-view Reconstruction Using an Adaptive Centroidal Voronoi Tessellation
Voronoi tessellations have recently emerged as an adaptive, space-partitioning discretization for shape and appearance reconstruction from multi-view images. However, the irregular cells they produce...
arXiv Computer Vision
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LoBE-GS: Load-Balanced and Efficient 3D Gaussian Splatting for Large-Scale Scene Reconstruction
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arXiv Computer Vision
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VolFill is a generative framework that reconstructs the complete 3D geometry of a scene from a single RGB image, addressing the challenge of inferring hidden structures. It uses a hybrid 3D VAE to encode sparse truncated unsigned distance function grids into a latent space, and a latent Diffusion Transformer to denoise and recover the full scene. By conditioning on a geometry foundation model, VolFill leverages spatial priors and provides a structured representation that enables direct surface extraction and scalable occupancy queries, outperforming existing amodal baselines on SCRREAM and NRGB-D datasets.
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Voronoi-Assisted Optimization for Diffusing Unsigned Distance Fields from Unoriented Points
The paper introduces VAD, a lightweight, network‑free method for computing Unsigned Distance Fields (UDFs) from unoriented point clouds. It assigns bi‑directional normals using two Voronoi‑based criteria, diffuses these normals to approximate a UDF gradient field, and then integrates to recover the final UDF. Experiments show VAD handles watertight, open, non‑manifold, and non‑orientable geometries efficiently and stably.
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CoMVS-GS: Collaborative Multi-View Stereo and 3D Gaussian Splatting for Surface Reconstruction
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