Monocular 3D Object Detection with Depth from Motion (2207.12988v2)

Abstract: Perceiving 3D objects from monocular inputs is crucial for robotic systems, given its economy compared to multi-sensor settings. It is notably difficult as a single image can not provide any clues for predicting absolute depth values. Motivated by binocular methods for 3D object detection, we take advantage of the strong geometry structure provided by camera ego-motion for accurate object depth estimation and detection. We first make a theoretical analysis on this general two-view case and notice two challenges: 1) Cumulative errors from multiple estimations that make the direct prediction intractable; 2) Inherent dilemmas caused by static cameras and matching ambiguity. Accordingly, we establish the stereo correspondence with a geometry-aware cost volume as the alternative for depth estimation and further compensate it with monocular understanding to address the second problem. Our framework, named Depth from Motion (DfM), then uses the established geometry to lift 2D image features to the 3D space and detects 3D objects thereon. We also present a pose-free DfM to make it usable when the camera pose is unavailable. Our framework outperforms state-of-the-art methods by a large margin on the KITTI benchmark. Detailed quantitative and qualitative analyses also validate our theoretical conclusions. The code will be released at https://github.com/Tai-Wang/Depth-from-Motion.

• The paper introduces a framework that leverages a stereo cost volume from temporally adjacent frames to improve monocular depth estimation.
• It employs a pose-free module with self-supervised egomotion estimation to overcome limitations inherent in single-frame approaches.
• The method achieves state-of-the-art performance on the KITTI benchmark, improving 3D detection average precision by up to 5.6%.

Monocular 3D Object Detection with Depth from Motion

The paper "Monocular 3D Object Detection with Depth from Motion" introduces a novel framework for 3D object detection using monocular input combined with temporal information from video sequences. The primary motivation behind this framework lies in overcoming the inherent challenges of monocular depth estimation, typically ill-posed in nature. The method leverages stereo geometry inferred from temporally adjacent frames, a concept inspired by binocular vision systems, to enhance depth estimation accuracy vital for 3D perception.

Technical Contributions

The authors present a thorough examination of monocular 3D detection conditions, emphasizing the crucial role of depth perception. Several key challenges such as cumulative errors in measurement, matching ambiguities, and static camera dilemmas are identified. To mitigate these, the paper proposes a novel framework, named Depth from Motion (DfM), which marries geometric insights from camera motion with monocular understanding.

1. Stereo Correspondence with Cost Volume: The framework constructs a geometry-aware stereo cost volume, transforming the depth estimation problem into a disparity estimation among a series of depth hypotheses, which provides a robust alternative to conventional single-frame depth estimations.
2. Pose-Free Extension: A significant practical consideration addressed is the absence of precise camera poses, for which the framework offers a pose-free variant by estimating egomotion using self-supervised learning. This makes the approach versatile and applicable in scenarios without available odometry data.
3. Monocular Compensation: To counteract the limitations of stereo methods in depth estimation, the authors integrate a monocular path that infers depth semantically using learned priors. This dual-path feature aggregation enables effective handling of diverse scenarios where pure stereo matching may falter.

Numerical Results and Analysis

The paper reports substantial empirical results on the KITTI benchmark, where the DfM model achieves state-of-the-art performance in the monocular domain, outperforming previous approaches considerably. Notably, it significantly improves 3D Average Precision (AP) metrics, specifically outperforming competitors by approximately 2.6% to 5.6% in various difficulty levels for 3D detection. The detailed ablation studies underscore the importance of each framework component, with particularly strong improvements observed from incorporating monocular compensation.

Impact and Implications

This paper holds both theoretical and practical implications. Theoretically, it extends understanding of stereo systems to general two-view temporal settings, presenting insights into the challenges and design considerations for depth-from-motion tasks. Practically, the proposed DfM framework offers a promising path forward for robust 3D perception using monocular cameras, which are notably more economical and widely deployable compared to multi-sensor approaches.

Future directions could explore optimizing this framework's computational efficiency to achieve real-time performance. Additionally, addressing moving object depth estimation with more specialized designs will be a critical next step. Furthermore, integrating this monocular 3D detection pipeline with downstream tasks such as object tracking and motion forecasting could expand its utility in autonomous driving and robotics.

In conclusion, this work provides a comprehensive approach towards enhancing depth estimation and 3D object detection capabilities using monocular inputs, offering significant potential in advancing computer vision applications reliant on cost-effective sensing methodologies.