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ATVNet: Adaptive Thin Cost Volume for Stereo Disparity Estimation

Recently, stereo models based on coarse-to-fine approaches have drastically alleviated the memory footprint and speed limitations of complex network models. However, previous designs used a uniformly defined range for disparity estimation, which ignored the difficulty of pixel matching in different regions and introduced many unnecessary matching candidates. In this paper, we construct Adaptive Thin Volume Network (ATVNet) to improve accuracy and reduce computation time. Firstly, multi-scale feature maps are extracted to construct a combined cost volume, then the rough disparity map is derived from the low-resolution volume regression. Finally, the Disparity Correction Range (DCR) module adaptively adjusts the matching space through uncertainty estimation, which constructs thin volumes for gradually retrieving a more accurate high-resolution disparity map. With the gradual improvement of volume resolution and the adaptive reduction of correction range, the disparity map is recovered in a coarser-to-fine strategy. When training and testing on the Scene Flow and KITTI 2015 datasets, our ATVNet model exceeds the mainstream end-to-end model in accuracy and efficiency.