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Greatest papers with code

On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach

26 Mar 2018NVIDIA-AI-IOT/redtail

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.

AUTONOMOUS VEHICLES STEREO DEPTH ESTIMATION

Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve

5 Dec 2019puzzlepaint/camera_calibration

In contrast, generic camera models allow for very accurate calibration due to their flexibility.

POSE ESTIMATION STEREO DEPTH ESTIMATION

Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving

ICLR 2020 mileyan/Pseudo_Lidar_V2

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

Anytime Stereo Image Depth Estimation on Mobile Devices

26 Oct 2018mileyan/AnyNet

Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints.

STEREO DEPTH ESTIMATION

Real-time self-adaptive deep stereo

CVPR 2019 CVLAB-Unibo/Real-time-self-adaptive-deep-stereo

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

Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers

5 Nov 2020mli0603/stereo-transformer

Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth.

STEREO DEPTH ESTIMATION

Bi3D: Stereo Depth Estimation via Binary Classifications

CVPR 2020 NVlabs/Bi3D

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.

AUTONOMOUS NAVIGATION QUANTIZATION STEREO DEPTH ESTIMATION

UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos

CVPR 2019 baidu-research/UnDepthflow

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