# Shelf-Supervised Multi-Modal Pre-Training for 3D Object Detection
## Supplementary Material

This folder contains the following:
- README.md: This file
- corl24_shelf_supervised_pretraining_supp.pdf: Further discussion of results and description of the experimental setup.
- cm3d/ : A folder containing the code to generate pseudolabels

### cm3d/
- src/nuscenes/gen_2d_masks_detic.py: Use this file to generate 2D masks for each nuscenes image with Detic. Make sure to change the env. variables at the top of the file.
- src/nuscenes/2d_to_3d.py: Use this to back project masked LiDAR points into 3D to generate 3D predictions. Make sure to change the env. variables at the top of the file.


- src/waymo/gen_2d_masks_detic.py: Use this file to generate 2D masks for each Waymo image with Detic. Make sure to change the env. variables at the top of the file.
- src/waymo/2d_to_3d.py: Use this to back project masked LiDAR points into 3D to generate 3D predictions. Make sure to change the env. variables at the top of the file.


Data (waymo and nuScenes) goes in cm3d/data/
Setup Detic and GroundingSAM in cm3d/
Use the src/nuscenes/requirements.txt file to create the environment.