Stratified Transformer for 3D Point Cloud Segmentation (2203.14508v1)

Abstract: 3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies. In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance. Specifically, we first put forward a novel key sampling strategy. For each query point, we sample nearby points densely and distant points sparsely as its keys in a stratified way, which enables the model to enlarge the effective receptive field and enjoy long-range contexts at a low computational cost. Also, to combat the challenges posed by irregular point arrangements, we propose first-layer point embedding to aggregate local information, which facilitates convergence and boosts performance. Besides, we adopt contextual relative position encoding to adaptively capture position information. Finally, a memory-efficient implementation is introduced to overcome the issue of varying point numbers in each window. Extensive experiments demonstrate the effectiveness and superiority of our method on S3DIS, ScanNetv2 and ShapeNetPart datasets. Code is available at https://github.com/dvlab-research/Stratified-Transformer.

Stratified Transformer for 3D Point Cloud Segmentation

The paper "Stratified Transformer for 3D Point Cloud Segmentation" presents a method aimed at enhancing 3D point cloud segmentation by efficiently capturing long-range dependencies, which traditional methods often fail to address. This is achieved through the introduction of a Stratified Transformer, which balances computational efficiency with the ability to process distant contextual information.

Key Contributions and Methods

1. Stratified Key Sampling Strategy: The primary innovation is a novel key sampling strategy. By sampling nearby points densely and distant points sparsely for each query point, the model effectively increases the receptive field, allowing for the integration of long-range dependencies with minimal computational overhead.
2. First-layer Point Embedding: To mitigate challenges due to irregular point arrangements, the authors propose a point embedding technique at the initial layer. This approach aggregates local information, aiding convergence and enhancing the model's performance.
3. Contextual Relative Position Encoding (cRPE): This addition dynamically captures position information through an adaptive positional bias, interacting with the semantic features to preserve spatial relationships.
4. Hierarchical Structure with Memory Efficiency: The model adopts a hierarchical structure and introduces a memory-efficient implementation to address the varying point numbers across windows, optimizing resource usage.

Experimental Evaluation

The Stratified Transformer was evaluated on several datasets, including S3DIS, ScanNetv2, and ShapeNetPart, demonstrating state-of-the-art performance. Particularly noteworthy is its achievement of 72.0% mIoU on S3DIS Area 5 and 73.7% on ScanNetv2—all significant improvements over previous methods. This shows that the model not only effectively captures long-range dependencies but also generalizes well across varied datasets.

Discussion

These enhancements address the limitations of traditional methods, which primarily aggregate local features without effectively modeling long-range contexts. By leveraging the Transformer architecture's inherent capability to process global information through self-attention, this method advances the state-of-the-art in point cloud segmentation.

Implications and Future Directions:

The implications of this research extend to practical applications in autonomous vehicles, augmented reality, and robotics. The findings suggest that further exploration could be directed towards optimizing the trade-offs between computational cost and performance, perhaps through adaptive mechanisms that dynamically adjust sampling strategies according to scene complexity.

In conclusion, this paper contributes significantly to the field of 3D point cloud segmentation, offering a robust solution to a persistent challenge through innovative design choices tailored for 3D data. Future research may explore integrating these mechanisms with other advanced neural architectures or applying them to other domains where spatial relationships are crucial.