OctFormer: Octree-based Transformers for 3D Point Clouds (2305.03045v2)

Abstract: We propose octree-based transformers, named OctFormer, for 3D point cloud learning. OctFormer can not only serve as a general and effective backbone for 3D point cloud segmentation and object detection but also have linear complexity and is scalable for large-scale point clouds. The key challenge in applying transformers to point clouds is reducing the quadratic, thus overwhelming, computation complexity of attentions. To combat this issue, several works divide point clouds into non-overlapping windows and constrain attentions in each local window. However, the point number in each window varies greatly, impeding the efficient execution on GPU. Observing that attentions are robust to the shapes of local windows, we propose a novel octree attention, which leverages sorted shuffled keys of octrees to partition point clouds into local windows containing a fixed number of points while permitting shapes of windows to change freely. And we also introduce dilated octree attention to expand the receptive field further. Our octree attention can be implemented in 10 lines of code with open-sourced libraries and runs 17 times faster than other point cloud attentions when the point number exceeds 200k. Built upon the octree attention, OctFormer can be easily scaled up and achieves state-of-the-art performances on a series of 3D segmentation and detection benchmarks, surpassing previous sparse-voxel-based CNNs and point cloud transformers in terms of both efficiency and effectiveness. Notably, on the challenging ScanNet200 dataset, OctFormer outperforms sparse-voxel-based CNNs by 7.3 in mIoU. Our code and trained models are available at https://wang-ps.github.io/octformer.

An Overview of OctFormer: Octree-Based Transformers for 3D Point Clouds

The presented paper introduces "OctFormer," a transformer-based architecture designed for efficient and effective learning on 3D point clouds. This approach offers a compelling alternative to traditional convolutional neural networks (CNNs) by leveraging an innovative octree attention mechanism that significantly reduces the computational demands typically associated with transformers. As a consequence, OctFormer provides a scalable solution applicable to large-scale 3D point cloud tasks, such as segmentation and object detection.

Key Contributions

The most critical innovation in OctFormer is the development of octree attention, which reorganizes the way self-attention is applied to point clouds. Challenges in applying standard transformers to point clouds arise due to their quadratic complexity concerning the number of elements involved. The paper addresses this by introducing octree-based methods for partitioning the point cloud into windows of a fixed number of points, each defined by an octree's structure rather than traditional cubic partitions. By doing so, it ensures uniform workload distribution among computing units, which is crucial for leveraging parallel processing on modern GPUs.

The architecture integrates these octree attention operations into a transformer model suited for 3D point clouds, showing that it achieves state-of-the-art performance on benchmarks like ScanNet and SUN RGB-D for tasks such as segmentation and detection. Significantly, it surpasses previous methods, both in terms of accuracy (e.g., 7.3 mIoU better than MinkowskiNet on the ScanNet200 dataset) and efficiency (running times improved by at least 17 times over previous transformer baselines).

Theoretical and Practical Implications

Theoretically, OctFormer challenges the conventional practice of using fixed-size windows in transformer models for 3D point clouds. By demonstrating that octree-based partitioning can maintain the necessary robustness in attention computations while being computationally advantageous, it opens avenues for more efficient model architectures in computational 3D vision.

Practically, this enables processing much larger point clouds within the limitations of GPU memory and computational budgets, making models like OctFormer feasible for real-world applications such as autonomous driving and AR/VR systems. The ability to scale efficiently with increased data sizes without sacrificing accuracy has clear technological benefits and suggests that the application of transformers in 3D vision tasks can be broadened considerably.

Future Directions

While OctFormer provides a novel framework and demonstrates superior performance, the area of 3D deep learning continues to rapidly evolve. Future work could explore pretraining large-scale general 3D models using OctFormer, enabling more robust cross-modality applications. Enhancements in positional encoding strategies may further improve the network's expressiveness and flexibility. Additionally, leveraging this architecture in 3D content generation tasks, potentially conditioned on inputs like images or texts, presents a promising research frontier.

In summary, OctFormer makes significant strides toward efficient and effective transformer-based learning for 3D point clouds, providing a strong foundation for future research and development in this evolving field.