PyTorch implementation of Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose

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Deep Learning NeuralVS
Overview

Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose

Release Notes

The official PyTorch implementation of Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose, publish on NeuralIPS 2021. Example figure

Installation

To install required libs:

git clone https://github.com/Angtian/NeuralVS.git
cd NeuralVS
pip install -r requirements.txt

We use the same data preprocess as NeMo does. You can run the data preprocess part in NeMo repo or run the following code:

git clone https://github.com/Angtian/NeMo.git
cd NeMo
chmod +x PrepareData.sh
./PrepareData.sh
cd ..
mv ./NeMo/data ./

Matching using Single Image

Here we provide the code to run the pose matching experiment using single anchor image (section 4.3). To run the experiment using ImageNet pretrained backbone:

python .\code\SingleAnchorMatching.py --do_plot

To run the experiment with other backbone:

python .\code\SingleAnchorMatching.py --load_path {Path_to_saved_model} --do_plot

Code for Semi-supervised learning

Coming Soon

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Comments
  • Question about camera coordinate

    Question about camera coordinate

    Hi Angtian, Thanks a lot for sharing your great works!

    I have a question about your code in ProcessCameraParameters.py - get_transformation_matrix function. I believe that transformation matrix can be compute as [R^T | - R^T@C] (C is camera position in world coordinate) and the function above seems like acting same way. But i'm confused about why only y coordinate of camera is reversed,

    136] C[0] = distance * math.cos(elevation) * math.sin(azimuth) 137] C[1] = -distance * math.cos(elevation) * math.cos(azimuth) 138] C[2] = distance * math.sin(elevation)

    Is it the coordinate change for the openGL rendering?

    opened by mlnyang 4
Owner
Angtian Wang
PhD student at Johns Hopkins University, my main focus includes Computer Vision and Deep Learning.
Angtian Wang
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