Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving (1812.07179v6)

Abstract: 3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise but expensive LiDAR technology. Approaches based on cheaper monocular or stereo imagery data have, until now, resulted in drastically lower accuracies --- a gap that is commonly attributed to poor image-based depth estimation. However, in this paper we argue that it is not the quality of the data but its representation that accounts for the majority of the difference. Taking the inner workings of convolutional neural networks into consideration, we propose to convert image-based depth maps to pseudo-LiDAR representations --- essentially mimicking the LiDAR signal. With this representation we can apply different existing LiDAR-based detection algorithms. On the popular KITTI benchmark, our approach achieves impressive improvements over the existing state-of-the-art in image-based performance --- raising the detection accuracy of objects within the 30m range from the previous state-of-the-art of 22% to an unprecedented 74%. At the time of submission our algorithm holds the highest entry on the KITTI 3D object detection leaderboard for stereo-image-based approaches. Our code is publicly available at https://github.com/mileyan/pseudo_lidar.

• The paper demonstrates that converting visual depth maps into pseudo-LiDAR point clouds significantly narrows the performance gap with LiDAR-based methods.
• It leverages advanced stereo disparity algorithms to achieve a detection accuracy improvement from 22% to 74% on the KITTI benchmark.
• The approach offers a cost-effective alternative to expensive sensors, enhancing safety and enabling robust sensor fusion in autonomous driving.

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

The task of robust and precise 3D object detection remains a cornerstone in autonomous driving technology, where the ability to accurately identify pedestrians, cyclists, and vehicles in the environment is critical for safe navigation. Historically, approaches leveraging LiDAR technology have dominated in terms of accuracy due to LiDAR's ability to produce highly precise 3D point clouds. However, the prohibitive costs and reliance on LiDAR create opportunities for alternative methods utilizing more ubiquitous, cost-effective sensors, such as cameras. This paper proposes a novel method named "Pseudo-LiDAR" that significantly narrows the performance gap between image-based and LiDAR-based 3D object detection by rethinking the representation of visual depth estimates.

Problem Statement

While previous methods using monocular or stereo imagery have suffered from drastically lower accuracies in 3D object detection, attributed largely to poor image-based depth estimation, this paper challenges that notion. Instead, it argues that the core issue may lie within the representation of depth data rather than its quality.

Approach and Methodology

The authors introduce a two-step approach to transform visual depth data into a high-fidelity 3D point cloud representation akin to LiDAR data, which they term "Pseudo-LiDAR." This method essentially converts image-produced depth maps into 3D point clouds, enabling the use of existing LiDAR-based 3D object detection frameworks.

1. Depth Estimation: Leveraging state-of-the-art stereo disparity estimation algorithms such as PSMNet, DispNet, and SPS-stereo, depth maps are computed from stereo image pairs.
2. Pseudo-LiDAR Generation: The estimated depth maps are back-projected into a 3D coordinate system to form dense point clouds. This back-projection preserves the physical coherence and scale invariance of objects, aligning with the attributes of actual LiDAR point clouds.
3. 3D Object Detection Algorithms: The generated pseudo-LiDAR point clouds are fed into established LiDAR-based 3D object detection pipelines such as AVOD and Frustum PointNet, emphasizing the compatibility and versatility of the proposed representation.

Experimental Results

The proposed pseudo-LiDAR method demonstrates remarkable improvements over existing image-based methods, significantly enhancing the performance of 3D object detection tasks on the KITTI benchmark. Notable results include:

• The detection accuracy of objects within a 30-meter range improved from 22% (state-of-the-art image-based) to 74% using pseudo-LiDAR.
• Pseudo-LiDAR-based methods outperformed previous image-based methods by a wide margin, doubling the performance on metrics such as 3D average precision (AP).

These results suggest that the primary cause of the performance gap between stereo and LiDAR systems is the data representation rather than the precision of depth estimation.

Implications and Future Directions

The transformation to pseudo-LiDAR has profound implications for the field of autonomous driving:

• Cost Reduction: By potentially replacing expensive LiDAR systems with more affordable stereo camera setups, the overall cost of autonomous vehicles can be significantly reduced.
• Enhanced Safety and Redundancy: Pseudo-LiDAR systems can serve as secondary safety measures in the event of LiDAR failure, ensuring continuous and reliable object detection.
• Sensor Fusion Opportunities: The pseudo-LiDAR framework opens avenues for advanced sensor fusion techniques, integrating dense pseudo-LiDAR with sparse LiDAR data to enhance detection accuracy and robustness.

Future research directions could address several critical areas:

1. Depth Estimation Accuracy: Further optimizing depth estimation algorithms, especially for distant objects, could bridge remaining gaps in detection accuracy.
2. High-Resolution Imaging: The application of high-resolution stereo cameras could improve detection performance for smaller and more distant objects by providing richer depth information.
3. Real-time Processing: Developing more efficient real-time processing frameworks for pseudo-LiDAR to ensure the practical applicability in dynamic driving conditions.

Conclusion

In conclusion, this paper highlights a significant advancement in the domain of 3D object detection by shifting the focus from raw depth precision to the representation of depth data. The pseudo-LiDAR approach marks a pivotal step towards making image-based 3D object detection viable, offering a cost-effective and potentially safer alternative to LiDAR systems. By aligning the strengths of stereo imagery with the robust detection capabilities of LiDAR-equivalent representations, this work brings us closer to the widespread adoption of autonomous driving technologies.