3D Object Detection Incorporating Instance Segmentation and Image Restoration

Nowadays, 3D object detection, which uses the color and depth information to find object localization in the 3D world and estimate their physical size and pose, is one of the most important 3D perception tasks in the field of computer vision. In order to solve the problem of mixed segmentation results when multiple instances appear in one frustum in the F-PointNet method and in the occlusion that leads to the loss of depth information, a 3D object detection approach based on instance segmentation and image restoration is proposed in this paper. Firstly, instance segmentation with Mask R-CNN on an RGB image is used to avoid mixed segmentation results. Secondly, for the detected occluded objects, we remove the occluding object first in the depth map and then restore the empty pixel region by utilizing the Criminisi Algorithm to recover the missing depth information of the object. The experimental results show that the proposed method improves the average precision score compared with the F-PointNet method.