UTTO uses uncertainty to guide test-time optimization with foundation model priors to enhance depth-only open-vocabulary 3D semantic segmentation without training, outperforming baselines on ScanNet datasets.
Open-vocabulary sam3d: Towards training-free open-vocabulary 3d scene understanding.arXiv preprint arXiv:2405.15580, 2024
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SAD-GS proposes dynamic geo-semantic anchoring via SAD and GSFL to learn reliable 3D semantic Gaussian fields, reporting best performance on LERF-OVS, 3D-OVS, and Mip-NeRF360 for open-vocabulary localization and segmentation.
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Privacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Segmentation Via Uncertainty-Guided Test-Time Optimization
UTTO uses uncertainty to guide test-time optimization with foundation model priors to enhance depth-only open-vocabulary 3D semantic segmentation without training, outperforming baselines on ScanNet datasets.
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SAD-GS: Learning Reliable 3D Semantic Gaussian Fields via Dynamic Geo-Semantic Anchoring
SAD-GS proposes dynamic geo-semantic anchoring via SAD and GSFL to learn reliable 3D semantic Gaussian fields, reporting best performance on LERF-OVS, 3D-OVS, and Mip-NeRF360 for open-vocabulary localization and segmentation.