Bi3D is a stereo depth estimation framework that estimates depth via a series of binary classifications. Rather than testing if objects are at a particular depth D, as existing stereo methods do, it classifies them as being closer or farther than D. It takes the stereo pair and a disparity $d\_{i}$ and produces a confidence map, which can be thresholded to yield the binary segmentation. To estimate depth on $N + 1$ quantization levels we run this network $N$ times and maximize the probability in Equation 8 (see paper). To estimate continuous depth, whether full or selective, we run the SegNet block of Bi3DNet for each disparity level and work directly on the confidence volume.
HITNet is a framework for neural network based depth estimation which overcomes the computational disadvantages of operating on a 3D volume by integrating image warping, spatial propagation and a fast high resolution initialization step into the network architecture, while keeping the flexibility of a learned representation by allowing features to flow through the network. The main idea of the approach is to represent image tiles as planar patches which have a learned compact feature descriptor attached to them. The basic principle of the approach is to fuse information from the high resolution initialization and the current hypotheses using spatial propagation. The propagation is implemented via a convolutional neural network module that updates the estimate of the planar patches and their attached features. In order for the network to iteratively increase the accuracy of the disparity predictions, the network is provided a local cost volume in a narrow band (±1 disparity) around the planar patch using in-network image warping allowing the network to minimize image dissimilarity. To reconstruct fine details while also capturing large texture-less areas we start at low resolution and hierarchically upsample predictions to higher resolution. A critical feature of the architecture is that at each resolution, matches from the initialization module are provided to facilitate recovery of thin structures that cannot be represented at low resolution.