TANet: Robust 3D Object Detection from Point Clouds with Triple Attention (1912.05163v1)

Abstract: In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches. We observe two crucial phenomena: 1) the detection accuracy of the hard objects, e.g., Pedestrians, is unsatisfactory, 2) when adding additional noise points, the performance of existing approaches decreases rapidly. To alleviate these problems, a novel TANet is introduced in this paper, which mainly contains a Triple Attention (TA) module, and a Coarse-to-Fine Regression (CFR) module. By considering the channel-wise, point-wise and voxel-wise attention jointly, the TA module enhances the crucial information of the target while suppresses the unstable cloud points. Besides, the novel stacked TA further exploits the multi-level feature attention. In addition, the CFR module boosts the accuracy of localization without excessive computation cost. Experimental results on the validation set of KITTI dataset demonstrate that, in the challenging noisy cases, i.e., adding additional random noisy points around each object,the presented approach goes far beyond state-of-the-art approaches. Furthermore, for the 3D object detection task of the KITTI benchmark, our approach ranks the first place on Pedestrian class, by using the point clouds as the only input. The running speed is around 29 frames per second.

• The paper's main contribution is introducing the Triple Attention (TA) and Coarse-to-Fine Regression (CFR) modules to enhance detection accuracy in noisy point clouds.
• It employs channel-wise, point-wise, and voxel-wise attention to suppress noise and highlight informative features, improving detection of small objects like pedestrians.
• Experimental results on the KITTI dataset show top performance with 29 fps, demonstrating strong potential for autonomous driving applications.

TANet: Robust 3D Object Detection from Point Clouds with Triple Attention

The paper presents a robust approach to 3D object detection in point clouds named TANet, which addresses some of the principal challenges in this area of research. By setting out to solve issues associated with detecting small, hard-to-identify objects like pedestrians and handling environments with substantial noise, TANet distinguishes itself as a significant contribution to the field of 3D object detection.

Key Innovations

TANet integrates two primary modules to enhance the detection accuracy in challenging scenarios: the Triple Attention (TA) module and the Coarse-to-Fine Regression (CFR) module.

1. Triple Attention Module (TA):
   • This module combines channel-wise, point-wise, and voxel-wise attention mechanisms. By adapting these three perspectives, the TA module leverages multi-level feature attention to focus on informative points and suppress noise. This attentional focus enhances the discriminative power of the feature representations, particularly valuable in cluttered or noisy environments.
2. Coarse-to-Fine Regression (CFR):
   • This module includes a novel approach for refining bounding box predictions. The initial coarse regression provides rough box estimations, followed by refinement through the Pyramid Sampling Aggregation (PSA) technique, which collects cross-layer features. This stratified strategy improves localization accuracy without incurring additional computational costs.

Experimental Results

The authors conduct rigorous evaluations on the KITTI dataset, demonstrating the robustness and efficiency of TANet, particularly under conditions of added noise. In scenarios with additional random noise points, TANet significantly outperforms existing state-of-the-art approaches. For example, on pedestrian detection tasks, it surpasses PointRCNN by a considerable margin, showing marked resilience to noising data.

For the KITTI benchmark, TANet achieves top-ranking results in pedestrian detection, using point clouds as its sole input, with an inference speed of approximately 29 frames per second. Its capacity to maintain high detection accuracy, even at high noise levels, underscores its potential for real-world applications, particularly in autonomous driving systems where accurate small object detection is critical.

Implications and Future Directions

The introduction of multi-level attention mechanisms for 3D object detection opens up various avenues for further exploration. Practically, the integration of TA and CFR modules could be generalized and applied to other domains where point cloud data is primary, such as in augmented reality and robotics. Theoretically, the results suggest that further refinement and combination of attention-based models could continue to yield improvements in processing point cloud data, offering a promising direction for future research.

Potential advancements could involve exploring other forms of attention or integrating additional data sources to complement point cloud information, further enhancing the robustness and applicability of TANet. Future research might also investigate how these strategies can be applied in different contexts or further optimized for various computational environments.

In conclusion, TANet represents a meaningful advance in the field of 3D object detection from point clouds, offering robust and accurate performance in challenging detection scenarios. By effectively leveraging the TA and CFR modules, this approach sets a strong precedent for the development of next-generation 3D detection models.