PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models

• URL: http://arxiv.org/abs/2403.06403v3
• Date: Thu, 18 Jul 2024 02:09:10 GMT
• Title: PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models
• Authors: Qingdong He, Jinlong Peng, Zhengkai Jiang, Xiaobin Hu, Jiangning Zhang, Qiang Nie, Yabiao Wang, Chengjie Wang,
• Abstract summary: We present PointSeg, a training-free paradigm that leverages off-the-shelf vision foundation models to address 3D scene perception tasks. PointSeg can segment anything in 3D scene by acquiring accurate 3D prompts to align their corresponding pixels across frames. Our approach significantly surpasses the state-of-the-art specialist training-free model by 14.1$%$, 12.3$%$, and 12.6$%$ mAP on ScanNet, ScanNet++, and KITTI-360 datasets.
• Score: 51.24979014650188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent success of vision foundation models have shown promising performance for the 2D perception tasks. However, it is difficult to train a 3D foundation network directly due to the limited dataset and it remains under explored whether existing foundation models can be lifted to 3D space seamlessly. In this paper, we present PointSeg, a novel training-free paradigm that leverages off-the-shelf vision foundation models to address 3D scene perception tasks. PointSeg can segment anything in 3D scene by acquiring accurate 3D prompts to align their corresponding pixels across frames. Concretely, we design a two-branch prompts learning structure to construct the 3D point-box prompts pairs, combining with the bidirectional matching strategy for accurate point and proposal prompts generation. Then, we perform the iterative post-refinement adaptively when cooperated with different vision foundation models. Moreover, we design a affinity-aware merging algorithm to improve the final ensemble masks. PointSeg demonstrates impressive segmentation performance across various datasets, all without training. Specifically, our approach significantly surpasses the state-of-the-art specialist training-free model by 14.1$\%$, 12.3$\%$, and 12.6$\%$ mAP on ScanNet, ScanNet++, and KITTI-360 datasets, respectively. On top of that, PointSeg can incorporate with various foundation models and even surpasses the specialist training-based methods by 3.4$\%$-5.4$\%$ mAP across various datasets, serving as an effective generalist model.

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