Ground-aware Monocular 3D Object Detection for Autonomous Driving (2102.00690v1)

Abstract: Estimating the 3D position and orientation of objects in the environment with a single RGB camera is a critical and challenging task for low-cost urban autonomous driving and mobile robots. Most of the existing algorithms are based on the geometric constraints in 2D-3D correspondence, which stems from generic 6D object pose estimation. We first identify how the ground plane provides additional clues in depth reasoning in 3D detection in driving scenes. Based on this observation, we then improve the processing of 3D anchors and introduce a novel neural network module to fully utilize such application-specific priors in the framework of deep learning. Finally, we introduce an efficient neural network embedded with the proposed module for 3D object detection. We further verify the power of the proposed module with a neural network designed for monocular depth prediction. The two proposed networks achieve state-of-the-art performances on the KITTI 3D object detection and depth prediction benchmarks, respectively. The code will be published in https://www.github.com/Owen-Liuyuxuan/visualDet3D

• The paper introduces ground plane priors to filter out unlikely 3D anchors, narrowing the search space to enhance object detection.
• It develops a ground-aware convolution module that leverages geometric depth cues from vertical pixel associations for improved depth perception.
• The approach achieves state-of-the-art results on the KITTI dataset, demonstrating significant accuracy gains in urban autonomous driving scenarios.

Ground-aware Monocular 3D Object Detection for Autonomous Driving

The paper "Ground-aware Monocular 3D Object Detection for Autonomous Driving" addresses the challenge of estimating three-dimensional positions and orientations of objects using a single RGB camera. This is particularly pertinent in urban autonomous driving where cost-effectiveness and robustness are crucial. Unlike LiDAR or stereo vision systems, which provide depth information via additional hardware, monocular setups offer a low-cost, versatile alternative despite the inherent difficulty of lacking explicit depth information.

Core Contributions

The authors introduce methodologies to integrate ground plane priors into monocular 3D object detection frameworks. The paper outlines two primary contributions:

1. Anchor Filtering: The authors propose filtering 3D anchors that deviate significantly from the assumed ground plane, thereby narrowing down the anchor search space. This approach focuses the network on likely object positions during both training and inference phases.
2. Ground-aware Convolution Module: Aimed at enhancing depth perception akin to human reasoning, this module incorporates depth priors derived from the geometric relationship between the camera, objects, and the ground plane. By utilizing depth as an additional feature map, the convolution module effectively captures depth cues from vertical pixel associations.

Results

The proposed network demonstrates strong performance on the KITTI dataset, achieving state-of-the-art results in both 3D object detection and depth prediction benchmarks. For example, it reports significant improvements over existing methods such as RTM3D and M3D-RPN, with 21.65% accuracy in the easy category of the KITTI test set. The integration of the ground-aware convolution module contributes to excellent depth perception and object localization capabilities.

Theoretical and Practical Implications

From a theoretical perspective, the integration of ground plane constraints represents a promising direction for improving the depth perception of monocular systems. By embedding floor-based priors within the network architecture, the proposed methods facilitate improved geometric understanding from a single camera input, which is crucial in scenarios with constrained budgets.

Practically, these advancements may catalyze the adoption of monocular solutions in real-world autonomous driving systems. The increased efficiency and reliability brought by such methodological enhancements can reduce reliance on expensive and complex sensor arrays.

Future Directions

The work presents a foundation for future exploration into more sophisticated modeling of scene geometry, including varying ground levels and complex urban environments. Furthermore, extension to more diverse datasets and scenarios could enhance the robustness and generalizability of the proposed approach. Subsequent research could investigate the fusion of ground-aware monocular approaches with other sensory data to address limitations in challenging lighting or weather conditions.

In summary, the paper delivers notable strides in monocular 3D detection by leveraging ground-aware strategies, paving the way for efficient deployment in autonomous driving applications.