PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction (2308.16896v1)

Abstract: Semantic segmentation in autonomous driving has been undergoing an evolution from sparse point segmentation to dense voxel segmentation, where the objective is to predict the semantic occupancy of each voxel in the concerned 3D space. The dense nature of the prediction space has rendered existing efficient 2D-projection-based methods (e.g., bird's eye view, range view, etc.) ineffective, as they can only describe a subspace of the 3D scene. To address this, we propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively and a PointOcc model to process them efficiently. Considering the distance distribution of LiDAR point clouds, we construct the tri-perspective view in the cylindrical coordinate system for more fine-grained modeling of nearer areas. We employ spatial group pooling to maintain structural details during projection and adopt 2D backbones to efficiently process each TPV plane. Finally, we obtain the features of each point by aggregating its projected features on each of the processed TPV planes without the need for any post-processing. Extensive experiments on both 3D occupancy prediction and LiDAR segmentation benchmarks demonstrate that the proposed PointOcc achieves state-of-the-art performance with much faster speed. Specifically, despite only using LiDAR, PointOcc significantly outperforms all other methods, including multi-modal methods, with a large margin on the OpenOccupancy benchmark. Code: https://github.com/wzzheng/PointOcc.

• The paper introduces a cylindrical tri-perspective view that projects LiDAR point clouds onto three cylindrical planes to enhance spatial feature extraction.
• It employs spatial group pooling and 2D backbones to retain structural details while reducing computational complexity.
• Evaluations on the OpenOccupancy benchmark show a 4.6 IoU and 3.8 mIoU improvement over leading methods, underscoring its efficacy.

PointOcc: Cylindrical Tri-Perspective View for Point-Based 3D Semantic Occupancy Prediction

In the field of autonomous driving, 3D semantic occupancy prediction remains a pivotal challenge, requiring both precise and comprehensive perception of the environment. The paper "PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction" proposes an innovative approach to address this challenge by utilizing a cylindrical tri-perspective view (TPV) to efficiently represent and process point cloud data. The authors present a methodology that harnesses the unique distance distribution properties of LiDAR point clouds, enabling more fine-grained modeling, particularly in regions closer to the sensor.

Methodology Overview

The methodology encompasses several key components, setting new benchmarks in the 3D semantic occupancy prediction domain:

1. Cylindrical Tri-Perspective View (TPV): The proposed TPV model effectively captures the spatial information by projecting the 3D point cloud into three cylindrical coordinate planes. Each of these planes represents a different perspective of the scene, facilitating comprehensive feature extraction while maintaining computational efficiency.
2. Spatial Group Pooling: To preserve structural details during the projection process, spatial group pooling is employed. This technique allows for better retention of 3D features when projecting to 2D planes, minimizing information loss.
3. 2D Backbone Utilization: By leveraging existing 2D convolutional networks, the approach achieves efficient processing of the TPV planes. This decision not only reduces the computational burden but also allows for incorporation of pretrained models, which can enhance performance.
4. Feature Aggregation and Classification: Features projected onto each cylindrical TPV plane are aggregated, subsequently forming a comprehensive feature set that can be classified without complex post-processing steps.

Numerical Results and Performance

The paper reports significant improvements over contemporary methodologies, particularly in speed and performance. On the OpenOccupancy benchmark, PointOcc demonstrates superior results, surpassing existing LiDAR-only and multi-modal (LiDAR + camera) methods by considerable margins in both Intersection over Union (IoU) and mean IoU (mIoU). Specifically, the approach achieves a 4.6 increase in IoU and 3.8 in mIoU against the leading multi-modal methods, indicating a robust capacity to accurately predict semantic occupancy using solely LiDAR data.

Implications and Future Directions

The PointOcc framework presents several implications for the field:

• Efficiency and Scalability: By capitalizing on the efficiency of 2D backbones, the model challenges existing paradigms that heavily rely on computationally intensive 3D operations. This opens pathways for real-time deployment in autonomous systems where processing resources may be limited.
• Adaptability to Sparse Data: The investigation into using cylindrical coordinates, which cater better to the uneven density of LiDAR point clouds, illustrates a promising direction for handling sparse data situations typical in autonomous driving scenarios.
• Fusion with Modalities: While the current version focuses solely on LiDAR input, there is potential for future integration with camera data, potentially enhancing understanding of complex environments.

Overall, the method provides a scalable and efficient solution to the ongoing challenges in autonomous vehicle perception, with prospects for refinement and application expansion. With continual advancements in AI and machine learning, models like PointOcc are likely to evolve, addressing the finer nuances of 3D perception and contributing to safer and more reliable autonomous navigation systems.