Fusion Based Holistic Road Scene Understanding
This work addresses road scene understanding for autonomous driving systems, presenting an incremental improvement by integrating data fusion and joint modeling.
The paper tackles holistic road scene understanding by fusing visual and range data within a conditional random field framework, achieving effective results as validated on the KITTI dataset with diverse scenarios.
This paper addresses the problem of holistic road scene understanding based on the integration of visual and range data. To achieve the grand goal, we propose an approach that jointly tackles object-level image segmentation and semantic region labeling within a conditional random field (CRF) framework. Specifically, we first generate semantic object hypotheses by clustering 3D points, learning their prior appearance models, and using a deep learning method for reasoning their semantic categories. The learned priors, together with spatial and geometric contexts, are incorporated in CRF. With this formulation, visual and range data are fused thoroughly, and moreover, the coupled segmentation and semantic labeling problem can be inferred via Graph Cuts. Our approach is validated on the challenging KITTI dataset that contains diverse complicated road scenarios. Both quantitative and qualitative evaluations demonstrate its effectiveness.