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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.

gcnet_2epoch_result1.png gcnet_2epoch_result2.png

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