A Continuous Occlusion Model for Road Scene Understanding
By Vikas Dhiman, Quoc-Huy Tran, Jason J. Corso, Manmohan Chandraker. 2016
32 citations recorded by the source corpus.
We present a physically interpretable, continuous threedimensional (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 (SFM) point tracks with potentially occluding 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 raw 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.
Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition
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Citations in the corpus
- Multi-view 3D Object Detection Network for Autonomous Driving (2016, methodology): “e with 3DOP, it generates 3D proposals from monocular images. [31,32] introduces a detailed geometry representation of objects using 3D wireframe models. To incorporate temporal information, some work[6,21] combine structure from motion and ground estimation to lift 2D detection boxes to 3D bounding”