A Continuous Occlusion Model for Road Scene Understanding

Vikas Dhiman, Quoc-Huy Tran, Jason J. Corso, Manmohan Chandraker; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4331-4339


We present a physically interpretable, continuous 3D model for handling occlusions with applications to road scene understanding. We probabilistically assign each point in space to an object with a theoretical modeling of the reflection and transmission probabilities for the corresponding camera ray. Our modeling is unified in handling occlusions across a variety of scenarios, such as associating structure from motion point tracks with potentially occluded objects or modeling object detection scores in applications such as 3D localization. For point track association, our model uniformly handles static and dynamic objects, which is an advantage over motion segmentation approaches traditionally used in multibody SFM. Detailed experiments on the KITTI dataset show the superiority of the proposed method over both state-of-the-art motion segmentation and a baseline that heuristically uses detection bounding boxes for resolving occlusions. We also demonstrate how our continuous occlusion model may be applied to the task of 3D localization in road scenes.

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author = {Dhiman, Vikas and Tran, Quoc-Huy and Corso, Jason J. and Chandraker, Manmohan},
title = {A Continuous Occlusion Model for Road Scene Understanding},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}