Rethinking Open-Vocabulary Segmentation of Radiance Fields in 3D Space
Authors: name: Hyunjee Lee, name: Youngsik Yun, name: Jeongmin Bae, name: Seoha Kim, name: Youngjung Uh

Understanding the 3D semantics of a scene is a fundamental problem for
various scenarios such as embodied agents. While NeRFs and 3DGS excel at
novel-view synthesis, previous methods for understanding their semantics have
been limited to incomplete 3D understanding: their segmentation results are 2D
masks and their supervision is anchored at 2D pixels. This paper revisits the
problem set to pursue a better 3D understanding of a scene modeled by NeRFs and
3DGS as follows. 1) We directly supervise the 3D points to train the language
embedding field. It achieves state-of-the-art accuracy without relying on
multi-scale language embeddings. 2) We transfer the pre-trained language field
to 3DGS, achieving the first real-time rendering speed without sacrificing
training time or accuracy. 3) We introduce a 3D querying and evaluation
protocol for assessing the reconstructed geometry and semantics together. Code,
checkpoints, and annotations will be available online. Project page:
https://hyunji12.github.io/Open3DRF