paper link: [2305.15694] Learning Occupancy for Monocular 3D Object Detection

We employ the design and framework of OpenPCDet, and follows the base setup in CaDDN, thanks for their great work!

Therefore, please follow the installation steps in OpenPCDet.

First, please follow the KITTI data file generation in CaDDN. You can find "depth_caddn" in CaDDN, and "lidar_depth" in Google Drive.

We do not use the heavy deeplibV3 backbone. Instead, we use the pre-trained dla34 backbone from DD3D. Note that the authors slightly modify the model. We re-map the key and provide it at Google Drive.

Then, train the model:

cd OccupancyM3D/OpenPCDet/tools

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --rdzv_endpoint=localhost:6400 --nproc_per_node=4  train.py --launcher pytorch  --cfg_file ./cfgs/kitti_models/OccupancyM3D.yaml \
  --sync_bn --workers 4 \
  --num_epochs_to_eval 20  \
  --extra_tag OccupancyM3D

Eval the model using the pre-trained model:

CUDA_VISIBLE_DEVICES=0 python test.py --cfg_file ./cfgs/kitti_models/OccupancyM3D.yaml \
  --extra_tag val \
  --batch_size 2 --workers 4 \
  --ckpt $pre-trained-model-path

To ease the usage, we provide the pre-trained model at: Google Drive

Here we give the comparison.

Some good cases and bad cases (marked using arrows)

KITTI results

Waymo results