PVT: Point-Voxel Transformer for Point Cloud Learning (2108.06076v4)

Abstract: The recently developed pure Transformer architectures have attained promising accuracy on point cloud learning benchmarks compared to convolutional neural networks. However, existing point cloud Transformers are computationally expensive since they waste a significant amount of time on structuring the irregular data. To solve this shortcoming, we present Sparse Window Attention (SWA) module to gather coarse-grained local features from non-empty voxels, which not only bypasses the expensive irregular data structuring and invalid empty voxel computation, but also obtains linear computational complexity with respect to voxel resolution. Meanwhile, to gather fine-grained features about the global shape, we introduce relative attention (RA) module, a more robust self-attention variant for rigid transformations of objects. Equipped with the SWA and RA, we construct our neural architecture called PVT that integrates both modules into a joint framework for point cloud learning. Compared with previous Transformer-based and attention-based models, our method attains top accuracy of 94.0% on classification benchmark and 10x inference speedup on average. Extensive experiments also valid the effectiveness of PVT on part and semantic segmentation benchmarks (86.6% and 69.2% mIoU, respectively).

Overview of "PVT: Point-Voxel Transformer for Point Cloud Learning"

The paper "PVT: Point-Voxel Transformer for Point Cloud Learning" introduces an innovative architecture designed for efficient point cloud learning, known as the Point-Voxel Transformer (PVT). The paper addresses the computational inefficiencies inherent in existing Transformer-based architectures when applied to point cloud data. This inefficiency arises primarily due to the data irregularity and the nonlinear computational complexities associated with processing such data using traditional methods. The proposed solution, PVT, leverages a novel Sparse Window Attention (SWA) module alongside two branches for local and global feature extraction—voxel-based and point-based—to deliver improved accuracy with reduced computational overhead.

Technical Details and Contributions

1. Sparse Window Attention Module: The SWA module is a key innovation in PVT, designed to efficiently process voxelized point cloud data. It achieves linear computational complexity concerning voxel resolution by locally computing self-attention within non-overlapping 3D windows. This method bypasses the invalid computations associated with empty voxels, significantly optimizing processing speed and resource consumption.
2. Dual Branch Architecture: PVT integrates both voxel-based and point-based approaches within a unified framework, capturing both local and global features of point clouds. The voxel-based branch focuses on aggregating coarse-grained local features, enhanced by the efficiency of the SWA module. Conversely, the point-based branch utilizes self-attention mechanisms, with innovations like Relative-Attention (RA) and External Attention (EA), tailored to varying scales of point clouds to maintain the integrity of fine-grained shapes.
3. Performance and Efficiency: The PVT architecture exhibits a significant improvement in inference speed and accuracy across various point cloud tasks, including classification and segmentation. The model achieves a notable top accuracy of 94.1% on the ModelNet40 classification benchmark, with a tenfold increase in inference speed compared to existing Transformer-based models. The architecture also demonstrates robust performance in semantic segmentation tasks.
4. Extensive Experimental Validation: The authors validate the effectiveness of PVT through comprehensive experiments, surpassing previous state-of-the-art models on benchmarks such as ShapeNet Part, S3DIS, and SemanticKITTI. These results affirm the superiority of the SWA module and the dual-branch architecture in handling complex 3D vision tasks.

Implications and Future Directions

The development of PVT represents a significant step forward in the field of point cloud learning, primarily due to its computational efficiency and scalability. Practically, the PVT model can be applied in domains requiring real-time processing, such as autonomous driving and robotics, where point clouds are ubiquitous.

Theoretical implications include the introduction of novel attention mechanisms—SWA and RA—and their potential application beyond 3D point clouds to similar challenges in 2D vision tasks or natural language processing, where sparse and irregular data structures are prevalent.

Future research can explore expansions or modifications of the PVT architecture to address complex environments that require scene understanding or integration with other sensory data streams. Additionally, further exploration into hardware-specific optimizations could enhance the real-world applicability of PVT, particularly in resource-constrained settings or edge computing scenarios.

In summary, the PVT model stands as a testament to the ongoing advancement within the domain of 3D neural networks, demonstrating that integrating innovative computational techniques with existing frameworks can lead to substantial improvements in both efficiency and accuracy.