|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large$-$a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving.
Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints.
SOTA for Stereo Depth Estimation on KITTI2012
In this paper, we propose UnOS, an unified system for unsupervised optical flow and stereo depth estimation using convolutional neural network (CNN) by taking advantages of their inherent geometrical consistency based on the rigid-scene assumption.
Recently, end-to-end trainable deep neural networks have significantly improved stereo depth estimation for perspective images.
Inspired by octave convolution, we divide image features into high and low spatial frequency features, and two cost volumes are generated from these using our proposed plane-sweeping module.
To remedy this drawback, this paper proposes a novel efficient attention mechanism, which is equivalent to dot-product attention but has substantially less memory and computational costs.