Convolutional neural network using multi-scale information for stereo matching cost computation
Jiahui Chen, Chun Yuan · 2016
Computing matching cost by Convolutional neural networks(CNNs) work well in fetching accurate dense disparity maps. But these methods still have problems: (1) they always employ equal weights for left and right images in convolutional layers, losing relational information of patches; (2) they don't solve the balance between patches' size and processing efficiency, the larger size the more information but slower. The proposed multi-scale CNN structured method fetches contexts by employing down-sampled images, this increases the matching accuracy without enlarging the input patch. A multi-scale cross-based aggregation algorithm is proposed to further refine the performances. Proposed method achieves challenging performance against state-of-the-art methods with an error rate of 4.77% in KITTI non-occluded section.