RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding
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Existing 3D scene understanding tasks have achieved high performance on close-set benchmarks but fail to handle novel categories in real-world applications. To this end, we propose a Regional Point-Language Contrastive learning framework, namely RegionPLC, for open-world 3D scene understanding, which equips models trained on closed-set datasets with open-vocabulary recognition capabilities. We propose dense visual prompts to elicit region-level visual-language knowledge from 2D foundation models via captioning, which further allows us to build dense regional point-language associations. Then, we design a point-discriminative contrastive learning objective to enable point-independent learning from captions for dense scene understanding. We conduct extensive experiments on ScanNet, ScanNet200, and nuScenes datasets. Our RegionPLC significantly outperforms previous base-annotated 3D open-world scene understanding approaches by an average of 11.6% and 6.6% for semantic and instance segmentation, respectively. It also shows promising open-world results in absence of any human annotation with low training and inference costs. Code will be released.
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