PLA: Language-Driven Open-Vocabulary 3D Scene Understanding
(2211.16312)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.
Overview
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The PLA framework addresses the limitation of traditional 3D scene understanding models by enabling the recognition and localization of unseen categories without direct annotated supervision.
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By leveraging vision-language models like CLIP and ViT-GPT2, the framework creates hierarchical 3D-caption pairs and employs contrastive learning to align 3D point embeddings with semantic-rich text.
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Experimental results on ScanNet and S3DIS datasets show significant improvements in semantic and instance segmentation tasks, with practical implications for robotic navigation, augmented reality, and human-machine interaction.
PLA: Language-Driven Open-Vocabulary 3D Scene Understanding
The paper "PLA: Language-Driven Open-Vocabulary 3D Scene Understanding" addresses a significant challenge in 3D scene understanding. Traditional 3D scene understanding models are constrained by their training data—they can only recognize categories within their training label space. This limitation severely hampers their practical applicability in real-world scenarios where encountering unseen categories is common. The research proposes a novel framework named Point-Language Association (PLA), aimed at enabling 3D scene understanding models to recognize and localize unseen categories without direct annotated supervision.
Methodology Overview
The core idea revolves around leveraging the success of vision-language (VL) foundation models. These models have significantly advanced 2D open-vocabulary tasks through pre-training on large-scale image-text pairs. The challenge, however, is the lack of similar large-scale 3D-text pairs. The research proposes bridging this gap by exploiting multi-view images of 3D scenes and using VL models to generate rich captions for these images. This allows for indirect association between 3D points and semantic-rich text.
Key components of the PLA framework include:
- VL Foundation Models: Utilization of state-of-the-art vision-language models like CLIP and ViT-GPT2 for generating captions from multi-view images derived from the 3D data.
- Hierarchical Point-Caption Pairs: The creation of hierarchical 3D-caption pairs through geometric constraints between 3D scenes and multi-view images. This hierarchy includes scene-level, view-level, and entity-level associations, facilitating fine-grained supervision signals for visual-semantic representation learning.
- Contrastive Learning: Use of contrastive learning to align the embeddings of points and associated text, thus enabling language-aware embeddings that connect 3D data to semantic-rich captions.
- Binary Calibration Module: A novel binary head is proposed to calibrate the binary probability of a point belonging to base or novel classes, which addresses the over-confidence issue of models trained on annotated data only.
Experimental Results and Analysis
The PLA framework was evaluated on two prominent datasets, ScanNet and S3DIS, for both semantic and instance segmentation tasks across multiple open-vocabulary partitions. The results are compelling:
- Semantic Segmentation: PLA achieved significant improvements over baseline methods. For example, on ScanNet, it outperformed baseline methods by 25.8% to 44.7% in harmonic mean IoU (hIoU). On S3DIS, improvements ranged from 14.5% to 50.4%.
- Instance Segmentation: PLA showed robust performance, with improvements between 14.5% to 50.4% in hAP$_{50}$ across various partitions on ScanNet and S3DIS.
- Zero-Shot Domain Transfer: The model trained on ScanNet exhibited strong transferability to S3DIS, outperforming the LSeg-3D baseline by 7.7% to 18.3% in mean IoU (mIoU) for semantic segmentation and 5.0% to 9.5% in mAP$_{50}$ for instance segmentation.
Implications and Future Developments
The implications of this research are profound for both theoretical and practical advancements in AI:
- Theoretical Contributions: The hierarchical point-caption association introduces a scalable and effective way to transfer knowledge from 2D vision-language models to 3D tasks. This method is generic and can be applied to various scene understanding tasks beyond the scope of the datasets and tasks evaluated.
- Practical Applications: The ability to recognize and localize unseen objects in 3D scenes has vast applications, from robotic navigation and manipulation to augmented reality and human-machine interaction. The framework can significantly enhance the autonomy and adaptability of systems operating in diverse and unstructured environments.
- Further Research: The study opens several avenues for future research. Enhancing the robustness of binary calibration for out-of-domain transfer, exploring more sophisticated methods to integrate heterogeneous caption supervisions, and reducing computational overhead while maintaining performance are critical next steps.
Conclusion
The PLA framework represents a substantial step forward in enabling open-vocabulary 3D scene understanding. By effectively distilling knowledge from pre-trained vision-language models and associating it with 3D data, the research bypasses the annotated-data bottleneck and demonstrates impressive performance in recognizing and localizing novel categories. This work not only addresses immediate challenges in 3D scene understanding but also sets the stage for future innovations in AI-driven perception systems.