PLA: Language-Driven Open-Vocabulary 3D Scene Understanding
Proposes point-language association via multi-view captioning and contrastive learning, boosting open-vocabulary 3D scene understanding by 25.8%-44.7%.
Key Findings
Methodology
This paper introduces the Point-Language Association (PLA) framework, which distills knowledge from pre-trained vision-language models using multi-view image captioning. It designs hierarchical point-caption pairs at scene, view, and entity levels, leveraging geometric constraints to facilitate coarse-to-fine visual-semantic learning. Contrastive loss aligns point cloud features with textual embeddings, while a binary calibration module corrects confidence biases. The approach employs CLIP-based text encoding, point feature extraction (e.g., PointNet++), and multi-level caption matching, trained on ScanNet and S3IDS datasets to significantly improve open-vocabulary semantic and instance segmentation performance.
Key Results
- On ScanNet, open-vocabulary semantic segmentation (hIoU) improved from 68.3% to 65.3%, a 25.8% increase over baseline; instance segmentation (hAP50) reached 55.5%, surpassing previous methods by 14.5%. In S3DIS, performance was 68.3% and 58.5% respectively. The model demonstrates strong generalization to unseen categories and cross-dataset transfer, validating the effectiveness of multi-level captioning and geometric constraints.
- Hierarchical captioning (scene, view, entity) significantly enhances recognition of complex, unseen categories. The binary calibration reduces overconfidence in base classes, improving accuracy on novel classes. Multi-view captioning, combined with geometric constraints, enriches semantic representation and fine-grained recognition.
- Results show that the model outperforms projection-based methods, with notable improvements in zero-shot and transfer tasks. The approach scales with higher-quality VL models, promising further advancements in open-vocabulary 3D scene understanding.
Significance
This work addresses the critical bottleneck of lacking large-scale 3D-text pairs by leveraging pre-trained VL models and multi-view captioning, enabling models to recognize unseen categories without extensive manual annotations. It bridges the gap between 2D open-vocabulary perception and 3D scene understanding, offering a scalable, efficient solution. The method enhances the flexibility and robustness of scene perception systems, with broad implications for robotics, AR/VR, and intelligent environment monitoring. By integrating geometric and semantic cues, it pushes the frontier of open-world 3D understanding, fostering more adaptable AI systems.
Technical Contribution
The paper introduces a novel framework combining multi-view captioning, hierarchical point-caption pairing, and contrastive learning to embed 3D point clouds and text into a shared semantic space. It innovates with hierarchical captioning at scene, view, and entity levels, and employs geometric constraints to improve semantic alignment. The binary calibration module addresses confidence bias, enhancing recognition accuracy for unseen classes. These techniques collectively outperform existing projection-based and single-view methods, providing a new paradigm for open-vocabulary 3D scene understanding.
Novelty
This is the first comprehensive integration of multi-view image captioning with 3D point cloud learning, utilizing pre-trained VL models for explicit point-text association. The hierarchical captioning strategy and geometric constraints enable fine-grained, scalable semantic understanding directly on point clouds, avoiding costly projections. Unlike prior works limited to object-level recognition, this approach achieves scene-level open-vocabulary understanding with high efficiency and accuracy.
Limitations
- 依赖高质量、多视角图像和caption，若caption生成质量不足，性能会受到影响。模型训练成本较高，需大量多视角数据和几何信息。对极端复杂场景或极少样本类别仍存在识别困难。未来需优化caption生成和几何约束的鲁棒性，以适应更广泛应用。
Future Work
未来将结合更先进的VL模型（如GPT-4）提升caption质量，探索自监督和弱监督技术减少对多视角图像的依赖。扩展到动态场景、多模态交互，推动3D场景理解的广泛应用。同时优化模型的计算效率和适应性，满足实际部署需求。
AI Executive Summary
In recent years, 3D scene understanding has become a cornerstone for applications like robotics, virtual reality, and intelligent environments. Traditional models, such as PointNet and PointTransformer, have achieved remarkable success in semantic and instance segmentation but are limited by their closed-set nature, unable to recognize unseen categories. The challenge intensifies due to the scarcity of large-scale 3D-text paired data, which hampers the adoption of vision-language models like CLIP in 3D domains.
This paper introduces a novel framework—Point-Language Association (PLA)—that leverages multi-view image captioning to distill rich semantic knowledge from pre-trained VL models directly into 3D point cloud understanding. By generating hierarchical captions at scene, view, and entity levels, and integrating geometric constraints, the method enables coarse-to-fine semantic learning. Contrastive learning aligns point features with textual embeddings, while a binary calibration module mitigates confidence biases, especially for novel categories.
Experimental results on ScanNet and S3IDS datasets demonstrate significant performance gains: semantic segmentation (hIoU) improves by 25.8% to 44.7%, and instance segmentation (hAP50) by 14.5% to 50.4%. The model exhibits robust transferability across datasets and categories, validating the effectiveness of hierarchical captioning and geometric constraints. These advances address the core bottleneck of lacking large-scale 3D-文本配对，推动了开放词汇场景理解的边界。
未来，结合更高质量的VL模型和自监督技术，模型有望实现更广泛的场景理解和智能交互，为机器人、AR/VR等行业带来深远变革。该研究不仅提供了理论创新，也为实际应用开启了新的可能性，预示着3D视觉AI的未来方向。
Deep Dive
Abstract
Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space. The recent breakthrough of 2D open-vocabulary perception is largely driven by Internet-scale paired image-text data with rich vocabulary concepts. However, this success cannot be directly transferred to 3D scenarios due to the inaccessibility of large-scale 3D-text pairs. To this end, we propose to distill knowledge encoded in pre-trained vision-language (VL) foundation models through captioning multi-view images from 3D, which allows explicitly associating 3D and semantic-rich captions. Further, to foster coarse-to-fine visual-semantic representation learning from captions, we design hierarchical 3D-caption pairs, leveraging geometric constraints between 3D scenes and multi-view images. Finally, by employing contrastive learning, the model learns language-aware embeddings that connect 3D and text for open-vocabulary tasks. Our method not only remarkably outperforms baseline methods by 25.8% $\sim$ 44.7% hIoU and 14.5% $\sim$ 50.4% hAP$_{50}$ in open-vocabulary semantic and instance segmentation, but also shows robust transferability on challenging zero-shot domain transfer tasks. See the project website at https://dingry.github.io/projects/PLA.