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Stereo Depth Estimation

7 papers with code · Computer Vision
Subtask of Depth Estimation

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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

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

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

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

14 Jun 2019mileyan/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

Learning to Adapt for Stereo

CVPR 2019 CVLAB-Unibo/Learning2AdaptForStereo

Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment.

AUTONOMOUS DRIVING STEREO DEPTH ESTIMATION

360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume

11 Nov 2019albert100121/360SD-Net

Recently, end-to-end trainable deep neural networks have significantly improved stereo depth estimation for perspective images.

STEREO DEPTH ESTIMATION

Octave Deep Plane-Sweeping Network: Reducing Spatial Redundancy for Learning-Based Plane-Sweeping Stereo

IEEE Access 2019 matsuren/octDPSNet

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.

STEREO DEPTH ESTIMATION