A plug-and-play view partitioning scheme using combinatorial graph partitioning on visual dissimilarity and approximated spatial dispersion makes VGGT scalable to large view collections with gains in pose estimation, depth prediction, and reconstruction.
arXiv preprint arXiv:2510.23928 (2025) 4
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VCS-SLAM introduces geometric validation of semantic observations via visibility consistency, boundary evidence, and ray uncertainty to improve fusion in 3D Gaussian SLAM.
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Diversity-aware View Partitioning for Scalable VGGT
A plug-and-play view partitioning scheme using combinatorial graph partitioning on visual dissimilarity and approximated spatial dispersion makes VGGT scalable to large view collections with gains in pose estimation, depth prediction, and reconstruction.
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VCS-SLAM: Geometry-Validated Semantic Evidence Fusion for 3D Gaussian SLAM
VCS-SLAM introduces geometric validation of semantic observations via visibility consistency, boundary evidence, and ray uncertainty to improve fusion in 3D Gaussian SLAM.