thesis

[[TOC]]

Contrastive learning in 3D

Setup

• Create a conda environment and install conda requirements from env.yaml (includes pip installs)
• MinkowskiEngine v0.5.4 through pip

Dataset

Download the full ScanNet dataset. Optionally select a subset of the scans by limiting the file list inside the script.

python scripts/download-scannet.py

• .sens files are used to obtain the color and depth images, and camera matrices
  • Run python scripts/extract_sens.py to extract color, depth and matrices from the .sens files.
• PLY files are used to create the voxel grid
• label and label-filt are the labels for color and depth images
  • Run python scripts/extract_zip.py to extract multiple zip files

ScanNet Baselines

2D Semantic Segmentation

RGB semantic segmentation with ENet.

python scripts/sem_seg/train_enet.py configs/sem_seg/enet_train.yml

3D Semantic Segmentation

Voxel Grid

Prepare the occupancy grid using

python scripts/sem_seg/prepare_occ_grid.py

The maximum grid size can be found with max_grid_size.py (not required when training on subvolumes).

python scripts/sem_seg/train_occgrid.py configs/sem_seg/occgrid_train.yml