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About this paper
Hyunjee Lee, Youngsik Yun, Jeongmin Bae, Seoha Kim, Youngjung Uh
This research redefines open-vocabulary segmentation of radiance fields from a 2D mask problem to a 3D volumetric task, achieving enhanced semantic understanding of 3D scenes. This approach enables real-time language-embedded rendering at 70.1 FPS and demonstrates higher accuracy in both 3D and 2D segmentation compared to previous methods.
Key insights
- True 3D volumetric segmentation, as opposed to 2D mask generation, provides a more accurate and spatially comprehensive semantic understanding of radiance fields.
- Direct point-wise semantic supervision and scale-free embeddings are critical for achieving robust 3D semantic representations and eliminating multi-view inconsistencies.
- A hybrid approach of learning semantics with NeRF and transferring them to 3DGS enables real-time open-vocabulary segmentation without significant computational overhead.
Summary by alphaXiv.
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