Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Last update: Dec 06, 2022

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Deep Learning Lac-GwcNet

Overview

Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Contributions

A novel pairwise feature LSP to extract structural information, which is beneficial for accurate matching especially when the illumination of the image pair is imbalanced
A novel disparity refinement method CSR (or DSR to save memory) to deal with outliers that are difficult to match, e.g. disparity discontinuities and occluded regions.

Dependencies：

Python 3.6
PyTorch 1.7.0
torchvision 0.3.0
SceneFlow
KITTI stereo 2015
KITTI stereo 2012

Training on SceneFlow

python train.py --data_path (your Scene Flow data folder)

Finetuning on KITTI

python KITTI_ft.py --data_path (your KITTI training data folder) --load_path (the path of the model trained on SceneFlow)

Owner

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Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Related tags

Overview

Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Contributions

Dependencies：

Training on SceneFlow

Finetuning on KITTI

Owner

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Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Related tags

Overview

Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Contributions

Dependencies：

Training on SceneFlow

Finetuning on KITTI

Owner

Learn other languages ​​using artificial intelligence with python.

rliable is an open-source Python library for reliable evaluation, even with a handful of runs, on reinforcement learning and machine learnings benchmarks.

Official Pytorch implementation of 6DRepNet: 6D Rotation representation for unconstrained head pose estimation.

A simple code to perform canny edge contrast detection on images.

Code for Transformers Solve Limited Receptive Field for Monocular Depth Prediction

Repo for the paper Extrapolating from a Single Image to a Thousand Classes using Distillation

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This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

Tree LSTM implementation in PyTorch

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Code for the ICASSP-2021 paper: Continuous Speech Separation with Conformer.

Unicorn can be used for performance analyses of highly configurable systems with causal reasoning

This code finds bounding box of a single human mouth.

DrQ-v2: Improved Data-Augmented Reinforcement Learning

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Automated image registration. Registrationimation was too much of a mouthful.

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Unofficial PyTorch implementation of Guided Dropout

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