Dot-product attention has wide applications in computer vision and natural language processing.
IMAGE CLASSIFICATION INSTANCE SEGMENTATION OBJECT DETECTION OBJECT RECOGNITION SEMANTIC SEGMENTATION STEREO DEPTH ESTIMATION
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.
In contrast, generic camera models allow for very accurate calibration due to their flexibility.
In contrast, generic camera models allow for very accurate calibration due to their flexibility.
In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation.
3D OBJECT DETECTION AUTONOMOUS DRIVING STEREO DEPTH ESTIMATION
Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints.
Ranked #1 on
Stereo Depth Estimation
on KITTI2015
Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs.
Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth.
Given a strict time budget, Bi3D can detect objects closer than a given distance in as little as a few milliseconds, or estimate depth with arbitrarily coarse quantization, with complexity linear with the number of quantization levels.
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.
4 MOTION SEGMENTATION OPTICAL FLOW ESTIMATION STEREO DEPTH ESTIMATION VISUAL ODOMETRY