liuzhengzhe/3D-to-2D-Distillation-for-Indoor-Scene-Parsing

CVPR 2021 Oral https://arxiv.org/abs/2104.02243

3D-to-2D Distillation for Indoor Scene Parsing

Introduction

This repository is the implementation for 3D-to-2D Distillation for Indoor Scene Parsing in CVPR 2021 (Oral). The code is based on PSPNet.

Usage

1. Requirement:

   • Hardware: 4 GPUs (better with >=11G GPU memory)
   • Software: PyTorch>=1.1.0, Python3, tensorboardX,

2. Clone the repository:

   git clone https://github.com/liuzhengzhe/3D-to-2D-Distillation-for-Indoor-Scene-Parsing

3. Train:

   • Download ScanNet-v2 2D data scannet_frames_25k and 3D features extracted from MinkowskiNet from Google Drive or [BaiduYun](Link：https://pan.baidu.com/s/1ho5rQdV9omfcts5rM3GAGg?pwd=4qlr Extract Code：4qlr), including folders named "eachfeature" and "featidx2". Then put to them in the data folder:

   • Ignore 20 unused categories in ScanNet and generate new ground truth

     python data_process1.py
     python data_process2.py

   • Download ImageNet pre-trained models provided by https://github.com/hszhao/semseg and put them under folder initmodel for weight initialization.

   • Specify the GPU used in config then do training:

     sh tool/train.sh scannet pspnet50

4. Test:

   • Download [trained segmentation models] from google drive or baiduyun (extract code: 3sfq) and put them under folder specified in config or modify the specified paths.

   • For full testing (get listed performance):

     sh tool/test.sh scannet pspnet50

5. Generate 3D features from other 3D semantic segmentation models

   • Run 3D semantic segmentation model and save the features in the "data/feat" folder. Each file contains a feature array for one point cloud with shape N*d, where N is the number of points and d is the dimension of the 3D feature. The order of features should follow the order of points of the ScanNet .ply file.

     cd data/
     python proj3dto2d_1.py
     python proj3dto2d_2.py

     Then you can train the model with other custom 3D feature.

Performance

Description: mIoU/mAcc/aAcc stands for mean IoU, mean accuracy of each class and all pixel accuracy respectively.

ScanNet-v2:

• Setting: train on train set and test on val set.

Backbone	mIoU/mAcc/pAcc(ms)
PSPNet50	0.5822/0.7083/0.8170.