Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers. (ICCV 2021 Oral)
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Updated
Aug 17, 2023 - Jupyter Notebook
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers. (ICCV 2021 Oral)
Non-official Pytorch implementation of the CREStereo(CVPR 2022 Oral).
A curated list of awesome Deep Stereo Matching resources
Pytorch implementation of ICRA 2020 paper "360° Stereo Depth Estimation with Learnable Cost Volume"
Small Python utility to compare and visualize the output of various stereo depth estimation algorithms
Python scripts performing stereo depth estimation using the CREStereo model in ONNX.
[CVPR 2023 Highlight] The Official Code for High-Frequency Stereo Matching Network
Python scripts form performing stereo depth estimation using the high res stereo model in PyTorch .
Python scripts form performing stereo depth estimation using the HITNET model in ONNX.
Python scripts performing stereo depth estimation using the Fast-ACVNet model in ONNX.
Python scripts for performing stereo depth estimation using the MobileStereoNet model in ONNX
Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite.
Python scripts for generating synthetic stereo depth data using the UnrealCV library.
CGI-Stereo Python Inference Demo
Python scripts form performing stereo depth estimation using the CoEx model in ONNX.
TF2 implementation of "End-to-End Learning of Geometry and Context for Deep Stereo Regression"
Python scripts for performing stereo depth estimation using the MobileStereoNet model in Tensorflow Lite.
This project focuses on harnessing the power of Pseudo-LiDARs and 3D computer vision for unmanned aerial vehicles (UAVs). By integrating Pseudo-LiDAR technology with Stereo Global Matching (SGBM) algorithms, we aim to enable UAVs to perceive their surroundings in three dimensions accurately.
2nd Assignment of Computer Vision by José Rosa and Ricardo Silva
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