End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection (2004.03080v2)

Abstract: Reliable and accurate 3D object detection is a necessity for safe autonomous driving. Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment, they are also prohibitively expensive for many settings. Recently, the introduction of pseudo-LiDAR (PL) has led to a drastic reduction in the accuracy gap between methods based on LiDAR sensors and those based on cheap stereo cameras. PL combines state-of-the-art deep neural networks for 3D depth estimation with those for 3D object detection by converting 2D depth map outputs to 3D point cloud inputs. However, so far these two networks have to be trained separately. In this paper, we introduce a new framework based on differentiable Change of Representation (CoR) modules that allow the entire PL pipeline to be trained end-to-end. The resulting framework is compatible with most state-of-the-art networks for both tasks and in combination with PointRCNN improves over PL consistently across all benchmarks -- yielding the highest entry on the KITTI image-based 3D object detection leaderboard at the time of submission. Our code will be made available at https://github.com/mileyan/pseudo-LiDAR_e2e.

• The paper introduces a unified framework that integrates depth estimation with 3D object detection to resolve training misalignments.
• It employs differentiable change-of-representation modules, including soft quantization, to seamlessly merge stereo depth and detection losses.
• Results on the KITTI dataset demonstrate significant performance gains, advancing stereo imaging for cost-effective autonomous driving.

End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

This paper presents a novel framework targeting the integration of image-based and LiDAR-based 3D object detection, relevant for applications such as autonomous driving. Traditionally, LiDAR sensors provide reliable 3D spatial information, yet due to cost and deployment restrictions, stereo cameras are explored as a more accessible alternative, despite the inherent accuracy trade-offs.

The core contribution of the paper is the development of an End-to-End Pseudo-LiDAR (E2E-PL) framework which reconciles the training dichotomy previously seen in pseudo-LiDAR setups. Unlike previous methodologies, which necessitated independent training of depth estimation and object detection systems, this framework employs differentiable Change of Representation (CoR) modules. This advancement facilitates unified, end-to-end training of both components and marks a notable enhancement in workflow adaptability and model performance.

Central to the framework are innovative CoR modules, including differentiable subsampling and a novel soft quantization technique. These adaptations ensure compatibility across a range of state-of-the-art networks, optimizing the 3D detection pipeline's robustness. The authors document that employing the E2E-PL framework in conjunction with PointRCNN results in significant performance gains on the KITTI dataset's image-based 3D object detection leaderboard. Numerical results demonstrate improved performance metrics, with the system yielding the highest recorded scores at the time of writing.

A significant technical challenge addressed by the paper is the alignment of training objectives between the depth estimators and the object detectors—an issue that compromised detection accuracy in independent training frameworks. The differentiable CoR modules allow the gradients from detection errors to directly inform depth estimation improvements, targeting areas crucial for object boundaries in 3D space. This alignment is illustrated as reducing far-object and object-boundary inaccuracies, which are common in traditional depth estimation models.

The results on the KITTI dataset signify a step forward in stereo camera usability for real-world applications such as autonomous driving. The integration of depth and detection losses, carefully balanced through empirical analysis, demonstrates a meticulous approach to neural network training dynamics. E2E-PL's adaptability to different LiDAR-based detector inputs—whether in point-cloud or voxelized form—suggests broad applicability and sets a precedent for future developments.

Looking ahead, future work could explore addressing limitations of stereo images, such as resolving occlusion barriers and leveraging higher-resolution data to further close the performance gap between stereo and LiDAR-based systems. Another avenue for exploration could involve optimizing the runtime performance of stereo depth estimation networks, facilitating real-time application in dynamic environments.

In conclusion, this paper highlights the E2E-PL as a versatile and high-performing advancement in image-based 3D object detection. By achieving end-to-end training congruity and incorporating differentiable modules, it not only sets a new benchmark for stereo-based systems but also pushes the bounds of image utility in AI, with theoretical and practical implications for cost-effective autonomous driving technologies.