CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP (2303.04748v2)

Abstract: Training a 3D scene understanding model requires complicated human annotations, which are laborious to collect and result in a model only encoding close-set object semantics. In contrast, vision-language pre-training models (e.g., CLIP) have shown remarkable open-world reasoning properties. To this end, we propose directly transferring CLIP's feature space to 3D scene understanding model without any form of supervision. We first modify CLIP's input and forwarding process so that it can be adapted to extract dense pixel features for 3D scene contents. We then project multi-view image features to the point cloud and train a 3D scene understanding model with feature distillation. Without any annotations or additional training, our model achieves promising annotation-free semantic segmentation results on open-vocabulary semantics and long-tailed concepts. Besides, serving as a cross-modal pre-training framework, our method can be used to improve data efficiency during fine-tuning. Our model outperforms previous SOTA methods in various zero-shot and data-efficient learning benchmarks. Most importantly, our model successfully inherits CLIP's rich-structured knowledge, allowing 3D scene understanding models to recognize not only object concepts but also open-world semantics.

Overview of "CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP"

The paper "CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP" presents a novel approach for 3D scene understanding by leveraging the vision-language pre-trained model CLIP without any additional supervision. The primary innovation lies in transferring CLIP’s feature space to a 3D scene understanding model, enabling recognition of open-vocabulary semantics and long-tailed concepts with no need for human annotations.

Methodology

The approach taken in this work includes several key steps:

1. Pixel-Level Feature Extraction:
   • Multi-scale Region Extraction: The input image is cropped at multiple scales to handle objects of varying sizes. This step aims to increase the feature resolution.
   • Local Feature Extraction: Each cropped sample is segmented into super-pixels to preserve object-level semantics. Subsequently, additional local classification tokens are introduced in the ViT encoder to aggregate information only from local patches within super-pixels.
2. Feature Distillation and 3D Projection:
   • 2D to 3D Feature Projection: The extracted pixel-level features from multiple RGB views are projected onto the 3D point clouds using the camera poses and other transformation matrices.
   • Feature Distillation: The 3D scene understanding model is trained via feature distillation by minimizing the cosine similarity distance between the learned point features and the target features derived from the 2D projections.
3. Application and Fine-Tuning:
   • The model, named CLIP-FO3D, can perform semantic segmentation without needing any labeled data and exhibits strong performance in zero-shot and data-efficient learning.

Results and Implications

The experimental results demonstrate the efficacy of CLIP-FO3D. The annotation-free approach achieves promising outcomes for semantic segmentation on the ScanNet and S3DIS datasets, significantly outperforming previous methods especially regarding open-vocabulary and long-tailed concepts.

• Strong Numerical Results:
  • On ScanNet, the method achieves a 30.2 mIoU score in annotation-free settings which is a notable enhancement over the MaskCLIP-3D baseline.
  • For extended vocabulary datasets, CLIP-FO3D maintains robust performance across Head, Common, and Tail classes, illustrating the generalization capability of the model.
• Zero-shot Learning Benchmarks:
  • CLIP-FO3D outperforms previous state-of-the-art zero-shot learning methods across various settings, showcasing improved hIoU scores particularly when the number of unseen classes is increased.
• Data-efficient Learning:
  • In limited annotation scenarios, CLIP-FO3D shows substantial improvement over training-from-scratch and other pre-training methods. This points to the model's efficiency in utilizing sparse data, which is crucial given the laborious nature of 3D data collection and annotation.

Open-World Scene Understanding

A significant implication of this research is the encoding of open-world knowledge within 3D scene understanding frameworks. Unlike models trained with annotations that can only recognize predefined object categories, CLIP-FO3D retains CLIP’s ability to link 3D scenes with extensive open-world semantics. This allows for practical applications that require understanding beyond object recognition, such as robot navigation in dynamic and unstructured environments.

Future Directions

The successful distillation of CLIP's feature space into 3D representations hints at several promising future research directions:

1. Integration with LLMs: Combining CLIP-FO3D with LLMs could further enhance contextual scene understanding, enabling more sophisticated applications like interactive environment querying and intelligent agent behaviors.
2. Real-time Adaptability: Addressing the computational demands of feature extraction and distillation for real-time applications represents a valuable extension of this work.
3. Cross-modal Extensions: Extending the paradigm to other modalities, such as audio-visual or haptic data, could pave the way for genuinely holistic scene understanding models.

In conclusion, "CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP" provides a substantial contribution to the field of 3D scene understanding by introducing an annotation-free approach that preserves the open-world knowledge encoded in CLIP. The strong numerical results and the potential for further developments underscore its relevance and impact in the domain of AI-driven 3D scene representation and understanding.