StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
This addresses the problem of efficient and accurate depth prediction for applications like robotics and autonomous driving, representing a novel method for a known bottleneck.
The paper tackles real-time stereo matching by introducing StereoNet, an end-to-end deep architecture that runs at 60 fps on an NVidia Titan X and produces high-quality, edge-preserved disparity maps with sub-pixel matching precision an order of magnitude higher than traditional approaches.
This paper presents StereoNet, the first end-to-end deep architecture for real-time stereo matching that runs at 60 fps on an NVidia Titan X, producing high-quality, edge-preserved, quantization-free disparity maps. A key insight of this paper is that the network achieves a sub-pixel matching precision than is a magnitude higher than those of traditional stereo matching approaches. This allows us to achieve real-time performance by using a very low resolution cost volume that encodes all the information needed to achieve high disparity precision. Spatial precision is achieved by employing a learned edge-aware upsampling function. Our model uses a Siamese network to extract features from the left and right image. A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model re-introduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks. Leveraging color input as a guide, this function is capable of producing high-quality edge-aware output. We achieve compelling results on multiple benchmarks, showing how the proposed method offers extreme flexibility at an acceptable computational budget.