The code is tested on Ubuntu 18.04.

• PyTorch v1.1, CUDA 10.0 and cuDNN v7.4
• tensorflow or tensorflow-gpu v1.14.0 (for the tensorboard)

The model in this repository is from torchvision which contains pretrained ResNet50-Backbone and then fine-tuned on ScanNet dataset. In order to obtain the data, please follow this README.

After downloading the dataset from ScanNet, place them under the /data folder. You will have 3 folders:

• frames_square for rgb images, depth images and semantics segmentation labels(which is not needed here)
• scannet_frame_labels contains pixelwise instance segmentation labels
• scannet_frame_bbox contains labels for bounding box, object_id, semantic_label_id

So your /data folder will look like this:

• data
  • frames_square
    • scene0000_01
      • color
      • depth
  • scannet_frame_labels
    • scene0000_01
      • instance-filt
        • 0.png
        • 1.png
  • scannet_frame_bbox
    • scene0000_01
      • 0.p
      • 20.p

Run batch_load_scannet_data.pyto organize the data so that it will be easy to use our dataloader to train the model.

By default, the log for tensorboard is stored in /train_log under the root folder of this repository. Simply cd to this repository and run:

tensorboard --logdir=./train_log

You might need to conda install -c anaconda future in your conda environment.

• Plot function for loss history
• Add other backbone network
• Add Adam optimizer