Segment2Regress: Monocular 3D Vehicle Localization in Two Stages

Abstract

High-quality depth information is required to perform 3D vehicle detection, consequently, there exists a large performance gap between camera and LiDAR-based approaches. In this paper, our monocular camera-based 3D vehicle localization method alleviates the dependency on high-quality depth maps by taking advantage of the commonly accepted assumption that the observed vehicles lie on the road surface. We propose a two-stage approach that consists of a segment network and a regression network, called Segment2Regress. For a given single RGB image and a prior 2D object detection bounding box, the two stages are as follows: 1) The segment network activates the pixels under the vehicle (modeled as four line segments and a quadrilateral representing the area beneath the vehicle projected on the image coordinate). These segments are trained to lie on the road plane such that our network does not require full depth estimation. Instead, the depth is directly approximated from the known ground plane parameters. 2) The regression network takes the segments fused with the plane depth to predict the 3D location of a car at the ground level. To stabilize the regression, we introduce a coupling loss that enforces structural constraints. The efficiency, accuracy, and robustness of the proposed technique are highlighted through a series of experiments and ablation assessments. These tests are conducted on the KITTI bird’s eye view dataset where Segment2Regress demonstrates state-of-the-art performance. Further results are available at https://github.com/LifeBeyondExpectations/Segment2Regress