自動運転のための地面を考慮した単眼3D物体検出
Ground-aware Monocular 3D Object Detection for Autonomous Driving
単眼RGB画像から3D物体検出を行う際、地面平面が奥行き推定に与える手がかりを活用するニューラルネットワークモジュールを提案し、KITTIベンチマークで最先端性能を達成した論文。
著者: Yuxuan Liu, Yuan Yixuan, Ming Liu
分類: cs.CV, cs.RO
原文アブストラクト
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
