EPO is a trackless, edge-map-alignment framework that refines pose estimates from 3D foundation models and matches or exceeds bundle-adjustment performance with substantially lower runtime and memory use.
In: Proceedings of the Computer Vision and Pattern Recognition Conference
2 Pith papers cite this work. Polarity classification is still indexing.
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Coupling tetrahedra to Gaussians and pruning via a vertex-shared continuous opacity field produces unified, single-component tetrahedral meshes suitable for FEM from multi-view images.
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EPO: Boosting 3D Foundation Models with Edge-based Pose Optimization
EPO is a trackless, edge-map-alignment framework that refines pose estimates from 3D foundation models and matches or exceeds bundle-adjustment performance with substantially lower runtime and memory use.
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HoloTetSphere: Unified TetSphere Mesh Reconstruction for Physical Simulations
Coupling tetrahedra to Gaussians and pruning via a vertex-shared continuous opacity field produces unified, single-component tetrahedral meshes suitable for FEM from multi-view images.