**GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding** **Authors:** Chengyao Wang, Li Jiang, Xiaoyang Wu, Zhuotao Tian, Bohao Peng, Hengshuang Zhao, Jiaya Jia **Abstract:** Self-supervised 3D representation learning aims to learn effective representations from large-scale unlabeled point clouds. Most existing approaches use point discrimination as the pretext task, which assigns matched points in two distinct views as positive pairs and unmatched points as negative pairs. However, this approach often results in semantically identical points having dissimilar representations, leading to a high number of false negatives and introducing a "semantic conflict" problem. To address this issue, we propose GroupContrast, a novel approach that combines segment grouping and semantic-aware contrastive learning. Segment grouping partitions points into semantically meaningful regions, enhancing semantic coherence and providing semantic guidance for the subsequent contrastive representation learning. Semantic-aware contrastive learning augments the semantic information extracted from segment grouping, helping to alleviate the "semantic conflict" problem. Extensive experiments on multiple 3D scene understanding tasks demonstrate that GroupContrast learns semantically meaningful representations and achieves promising transfer learning performance. **Key Contributions:** - **Semantic-aware Contrastive Learning:** Integrates positive pairs within the same group and negative pairs from different groups to improve the pretext task of point discrimination. - **Segment Grouping:** Enhances semantic coherence among points within a scene and provides semantic guidance for contrastive learning through deep clustering. - **State-of-the-Art Performance:** Achieves superior transfer learning results in various 3D scene perception tasks, outperforming current state-of-the-art self-supervised 3D representation learning approaches. **Experiments:** - **Ablation Studies:** Demonstrates the effectiveness of each component of GroupContrast. - **Comparison with State-of-the-Art:** Shows superior performance in 3D semantic segmentation, instance segmentation, and object detection tasks. - **Data Efficiency:** Evaluates the data efficiency of GroupContrast on the ScanNet Data Efficient Semantic Segmentation benchmark, achieving state-of-the-art performance. **Conclusion:** GroupContrast is a self-supervised representation learning framework for 3D scene understanding, combining segment grouping and semantic-aware contrastive learning. It effectively decomposes a point cloud into multiple semantically meaningful regions, improving semantic-level recognition. Extensive experimental results demonstrate its promising transfer learning performance on various 3D scene understanding tasks. Future work includes exploring cross-dataset pre-training and collaborating with well-trained visual foundation models to enhance generalizability and robustness.**GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding** **Authors:** Chengyao Wang, Li Jiang, Xiaoyang Wu, Zhuotao Tian, Bohao Peng, Hengshuang Zhao, Jiaya Jia **Abstract:** Self-supervised 3D representation learning aims to learn effective representations from large-scale unlabeled point clouds. Most existing approaches use point discrimination as the pretext task, which assigns matched points in two distinct views as positive pairs and unmatched points as negative pairs. However, this approach often results in semantically identical points having dissimilar representations, leading to a high number of false negatives and introducing a "semantic conflict" problem. To address this issue, we propose GroupContrast, a novel approach that combines segment grouping and semantic-aware contrastive learning. Segment grouping partitions points into semantically meaningful regions, enhancing semantic coherence and providing semantic guidance for the subsequent contrastive representation learning. Semantic-aware contrastive learning augments the semantic information extracted from segment grouping, helping to alleviate the "semantic conflict" problem. Extensive experiments on multiple 3D scene understanding tasks demonstrate that GroupContrast learns semantically meaningful representations and achieves promising transfer learning performance. **Key Contributions:** - **Semantic-aware Contrastive Learning:** Integrates positive pairs within the same group and negative pairs from different groups to improve the pretext task of point discrimination. - **Segment Grouping:** Enhances semantic coherence among points within a scene and provides semantic guidance for contrastive learning through deep clustering. - **State-of-the-Art Performance:** Achieves superior transfer learning results in various 3D scene perception tasks, outperforming current state-of-the-art self-supervised 3D representation learning approaches. **Experiments:** - **Ablation Studies:** Demonstrates the effectiveness of each component of GroupContrast. - **Comparison with State-of-the-Art:** Shows superior performance in 3D semantic segmentation, instance segmentation, and object detection tasks. - **Data Efficiency:** Evaluates the data efficiency of GroupContrast on the ScanNet Data Efficient Semantic Segmentation benchmark, achieving state-of-the-art performance. **Conclusion:** GroupContrast is a self-supervised representation learning framework for 3D scene understanding, combining segment grouping and semantic-aware contrastive learning. It effectively decomposes a point cloud into multiple semantically meaningful regions, improving semantic-level recognition. Extensive experimental results demonstrate its promising transfer learning performance on various 3D scene understanding tasks. Future work includes exploring cross-dataset pre-training and collaborating with well-trained visual foundation models to enhance generalizability and robustness.