Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding (2409.03757v3)

Abstract: Complex 3D scene understanding has gained increasing attention, with scene encoding strategies playing a crucial role in this success. However, the optimal scene encoding strategies for various scenarios remain unclear, particularly compared to their image-based counterparts. To address this issue, we present a comprehensive study that probes various visual encoding models for 3D scene understanding, identifying the strengths and limitations of each model across different scenarios. Our evaluation spans seven vision foundation encoders, including image-based, video-based, and 3D foundation models. We evaluate these models in four tasks: Vision-Language Scene Reasoning, Visual Grounding, Segmentation, and Registration, each focusing on different aspects of scene understanding. Our evaluations yield key findings: DINOv2 demonstrates superior performance, video models excel in object-level tasks, diffusion models benefit geometric tasks, and language-pretrained models show unexpected limitations in language-related tasks. These insights challenge some conventional understandings, provide novel perspectives on leveraging visual foundation models, and highlight the need for more flexible encoder selection in future vision-language and scene-understanding tasks. Code: https://github.com/YunzeMan/Lexicon3D

• The paper presents a comprehensive evaluation of seven visual foundation models across vision-language scene reasoning, grounding, segmentation, and registration tasks.
• It demonstrates that self-supervised models like DINOv2 excel in both global and object-level tasks, while video models outperform in instance discrimination.
• The study underscores the potential of combining multiple encoder features to enhance generalization and efficiency in complex 3D scene understanding.

Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding

Overview

The paper "Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding" represents a comprehensive paper on visual encoding strategies and their effectiveness in 3D scene understanding tasks. This work meticulously evaluates a suite of visual foundation models from various domains—images, videos, and 3D point cloud data. It aims to identify the strengths and limitations of each model across different 3D scene understanding scenarios. The research focuses on four key tasks: Vision-Language Scene Reasoning, Visual Grounding, Semantic Segmentation, and Registration, covering a spectrum of global and object-level scene comprehension requirements.

Key Findings

1. Evaluation of Visual Foundation Models: The paper evaluates seven foundation models: DINOv2, LSeg, CLIP, StableDiffusion, V-JEPA, StableVideoDiffusion, and Swin3D. Each model was tested across the four aforementioned tasks to determine its efficacy in 3D scene understanding.
2. Superior Performance of DINOv2: DINOv2, which employs self-supervised learning (SSL) strategies, demonstrated the best overall performance among the image-based models, showcasing strong generalization and flexibility in both global and object-level tasks.
3. Video Models' Strengths: Video-based models like V-JEPA and StableVideoDiffusion showed superior performance in tasks involving object-level reasoning, particularly those requiring instance discrimination. This can be attributed to their ability to handle temporally continuous input frames effectively.
4. Limitations of Language-Pretrained Models: Models pretrained with language guidance (such as LSeg and CLIP) did not perform as well as expected in language-related tasks. This finding challenges the conventional preference for using such models in vision-language reasoning tasks.
5. Advantages of Diffusion Models: Generative pretrained models like StableDiffusion excel in geometrical understanding and registration tasks, offering new possibilities for utilizing visual foundation models in 3D scene understanding.

Implications and Future Directions

The results of this paper provide critical insights into the practical applications and theoretical implications of utilizing visual foundation models for 3D scene understanding:

1. Flexible Encoder Selection: The findings advocate for more flexible encoder selection methodologies in future vision-language and scene understanding tasks to optimize both performance and generalization.
2. Potential for Enhancing 3D Foundation Models: Given the superior performance of 2D models like DINOv2 in 3D tasks, there is an evident need to focus on improving the generalizability of 3D foundation models by leveraging larger datasets and more diverse pretraining protocols.
3. Combination of Multiple Encoders: The paper suggests that combining features from different foundational models could potentially enhance performance in 3D scene understanding tasks. This points towards a future where mixture-of-experts models might be developed for more robust scene comprehension.
4. Efficiency and Complexity Analysis: The complexity analysis indicates that while image-based models require substantial processing time for scene-level embeddings, 3D point encoders are more efficient but currently underperform due to limited training data. Balancing performance with computational efficiency will be crucial in future research and application development.

Conclusion

The paper "Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding" delivers an extensive evaluation of visual foundation models, revealing significant insights into their capabilities and limitations in complex 3D scene understanding tasks. The paper highlights the superior performance of models like DINOv2 and the strengths of video models in specific tasks, while also pointing out the unexpected limitations of language-pretrained models. These findings underscore the importance of flexible encoder selection and the potential benefits of leveraging multiple encoders, setting the stage for future advancements in AI-driven 3D scene understanding.