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 IEEE/CVF conference on computer vision and pattern recognition
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Current densification methods in 3D Gaussian Splatting do not significantly benefit from dense initializations and perform similarly to sparse SfM-based ones.
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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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The Role and Relationship of Initialization and Densification in 3D Gaussian Splatting
Current densification methods in 3D Gaussian Splatting do not significantly benefit from dense initializations and perform similarly to sparse SfM-based ones.