An Edgeconstrained Iterative Cost Aggregation Method for Stereo Matching To improve the accuracy of stereo matching in lowtextured or depth discontinuous regions of images, a new method using edgeconstrained iterative cost aggregation is proposed in this paper. Firstly, a bilateral diffusion is used as the preprocessing to reduce inconsistency in the matching direction, which can make the followup matching more reliable. Secondly, a matching cost function that combines both color and edge is adopted, and a twostep iterative cost aggression based on the minimum spanning tree (MST) is then proposed. In the first cost aggregation, to avoid accumulation of small weights happened in lowtextured regions, an enhanced weight function with coefficient adjustment is presented. And in the second cost aggregation, to appropriately provide weights to neighbors, an edge constraint is introduced. The edge information used in the second aggregation is produced by the random forest method from the disparity map obtained in the first cost aggregation. Leftright consistency check and epipolar constraint are both adopted to further eliminate false edge points. Finally, the disparity refinement is utilized to optimize the disparity map. The proposed method is conducted on Middlebury v.2 data set. Experimental results demonstrate that the proposed method can achieve higher matching accuracy, compared with other five stateofart methods.

3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization The complementary characteristics of active and passive depth sensing techniques motivate the fusion of the LiDAR sensor and stereo camera for improved depth perception. Instead of directly fusing estimated depths across LiDAR and stereo modalities, we take advantages of the stereo matching network with two enhanced techniques: Input Fusion and Conditional Cost Volume Normalization (CCVNorm) on the LiDAR information. The proposed framework is generic and closely integrated with the cost volume component that is commonly utilized in stereo matching neural networks. We experimentally verify the efficacy and robustness of our method on the KITTI Stereo and Depth Completion datasets, obtaining favorable performance against various fusion strategies. Moreover, we demonstrate that, with a hierarchical extension of CCVNorm, the proposed method brings only slight overhead to the stereo matching network in terms of computation time and model size.