Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving (1906.06310v3)

Abstract: Detecting objects such as cars and pedestrians in 3D plays an indispensable role in autonomous driving. Existing approaches largely rely on expensive LiDAR sensors for accurate depth information. While recently pseudo-LiDAR has been introduced as a promising alternative, at a much lower cost based solely on stereo images, there is still a notable performance gap. In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation. Concretely, we adapt the stereo network architecture and loss function to be more aligned with accurate depth estimation of faraway objects --- currently the primary weakness of pseudo-LiDAR. Further, we explore the idea to leverage cheaper but extremely sparse LiDAR sensors, which alone provide insufficient information for 3D detection, to de-bias our depth estimation. We propose a depth-propagation algorithm, guided by the initial depth estimates, to diffuse these few exact measurements across the entire depth map. We show on the KITTI object detection benchmark that our combined approach yields substantial improvements in depth estimation and stereo-based 3D object detection --- outperforming the previous state-of-the-art detection accuracy for faraway objects by 40%. Our code is available at https://github.com/mileyan/Pseudo_Lidar_V2.

• The paper leverages a stereo depth network that directly estimates depth to overcome errors in disparity-based methods.
• It introduces a depth cost volume and graph-based depth correction algorithm to systematically reduce depth estimation errors.
• The integration of sparse 4-beam LiDAR with stereo images achieves up to a 40% improvement in far-distance 3D object detection accuracy on the KITTI dataset.

Summary of "Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving"

The paper "Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving" addresses critical challenges in 3D object detection for autonomous vehicles using stereo images. The authors propose enhancements to the pseudo-LiDAR framework, which was designed to offer a cost-effective alternative to expensive LiDAR sensors by relying on stereo images. Their contributions focus on improving depth estimation accuracy, particularly for distant objects—a known weakness of existing pseudo-LiDAR approaches.

Key Contributions

1. Direct Depth Estimation: The authors emphasize that existing approaches predominantly rely on disparity estimation, which leads to systematic depth estimation errors, especially for faraway objects. In contrast, they propose a stereo depth network (SDN) that directly estimates depth by adjusting both the network architecture and the loss function.
2. Depth Cost Volume: To address the depth-to-disparity inconsistency, the authors develop a depth cost volume, enabling 3D convolutions that operate uniformly over depth scales. This adjustment helps in reducing the error margin, particularly visible in distant objects.
3. Integration with Sparse LiDAR: The paper suggests leveraging a sparse 4-beam LiDAR, which, although insufficient on its own, can significantly reduce depth estimation bias when combined with stereo images. This integration is achieved through a novel graph-based depth correction (GDC) algorithm.
4. Empirical Validation: The authors validate their approach on the KITTI dataset, demonstrating superior performance by reducing depth estimation errors for faraway objects. Their method achieves up to a 40% increase in detection accuracy for such objects compared to previous state-of-the-art methods.

Implications and Future Directions

The results highlight the practicality of combining stereo camera systems with sparse LiDAR, potentially revolutionizing autonomous vehicle sensor configurations by significantly reducing costs without compromising accuracy. The paper also opens avenues for further exploration in robust depth estimation algorithms that can seamlessly integrate heterogeneous data sources.

Additionally, the successful implementation of the GDC algorithm paves the way for exploring graph-theoretic approaches in other domains of AI, particularly in situations where data fusion is essential. Future work might focus on optimizing these algorithms for real-time implementations on vehicular systems.

The paper underscores a pivotal shift towards affordable and reliable autonomous driving technology, demonstrating that with appropriate algorithmic advancements, stereo camera systems can become viable alternatives to expensive LiDAR configurations. These contributions form a cornerstone for ongoing research and development in AI-driven autonomous navigation solutions.