HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching

Understand

This paper presents HITNet, a novel neural network architecture for real-time stereo matching.

• Contrary to many recent neural network approaches that operate on a full cost volume and rely on 3D convolutions, our approach does not explicitly build a volume and instead relies on a fast multi-resolution initialization step, differentiable 2D geometric propagation and warping mechanisms to infer disparity hypotheses.
• To achieve a high level of accuracy, our network not only geometrically reasons about disparities but also infers slanted plane hypotheses allowing to more accurately perform geometric warping and upsampling operations.
• Our architecture is inherently multi-resolution allowing the propagation of information across different levels.