GC Net Tensorflow2
Intro
This is a Work In Progress Tensorflow implementation of "End-to-End Learning of Geometry and Context for Deep Stereo Regression"
Below picture shows the training samples after 2 epochs in Tensorboard over Sceneflow dataset. It's huge dataset and my potato laptop can't handle more epochs.
Installation
Normally here I'd give instruction to install but Tensorflow installation is complicated. So I can only tell you that this work is done in tensorflow 2.2.2, Ubuntu 20.04, CUDA 10.1, CUDNN 7.6.5
See https://www.tensorflow.org/install/source#gpu for more info about Tensorflow version related to CUDA+CUDNN
Training
python train.py --cfg config/potato_laptop.yaml
Inference
TODO:
Status:
What's working?
- Full GC Net network
- Cost Volume -> Provide better information of disparity
- Soft Argmin / Soft Argmax -> Provide subpixel disparity
- Customizable Config through YAML
- Preprocessing
- Random Crop based on the network training height and width -> Can handle more epochs without overfitting (but my potato laptop can't handle it)
- Tensorboard images and loss logging
- Checkpoint save file