Open-vocabulary segmentation, powered by large visual-language models like CLIP, has expanded 2D segmentation capabilities beyond fixed classes predefined by the dataset, enabling zero-shot understanding across diverse scenes. Extending these capabilities to 3D segmentation introduces challenges, as CLIP's image-based embeddings often lack the geometric detail necessary for 3D scene segmentation. Recent methods tend to address this by introducing additional segmentation models or replacing CLIP with variations trained on segmentation data, which lead to redundancy or loss on CLIP's general language capabilities. To overcome this limitation, we introduce SPNeRF, a NeRF based zero-shot 3D segmentation approach that leverages geometric priors. We integrate geometric primitives derived from the 3D scene into NeRF training to produce primitive-wise CLIP features, avoiding the ambiguity of point-wise features. Additionally, we propose a primitive-based merging mechanism enhanced with affinity scores. Without relying on additional segmentation models, our method further explores CLIP's capability for 3D segmentation and achieves notable improvements over original LERF.
View on arXiv@article{hu2025_2503.15712,
title={ SPNeRF: Open Vocabulary 3D Neural Scene Segmentation with Superpoints },
author={ Weiwen Hu and Niccolò Parodi and Marcus Zepp and Ingo Feldmann and Oliver Schreer and Peter Eisert },
journal={arXiv preprint arXiv:2503.15712},
year={ 2025 }
}