Detecting Obstacle in 3D Space using Monocular Camera

Muhamad Amirul Haq, Shanq-Jang Ruan, Jiunhan Chen

Abstract: Monocular 3D object detection attempts to predict object location and dimension in 3D space using a single optical camera. The main challenge of monocular 3D object detection is the lack of depth information to infer the object’s distance. In this work, we propose a monocular 3D object detection based on CenterNet with discrete depth and encoded orientation angle. Our proposed method is able to achieve 54.6% detection score for car class on the challenging Cityscapes autonomous driving dataset, outperforming prior monocular 3D object detection by a convincing margin.