🍾 POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images

Antonin Vobecky  Oriane Siméoni  David Hurych  Spyros Gidaris  Andrei Bursuc  Patrick Pérez  Josef Sivic 

Code for paper "POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images"

Welcome to the official implrmrntation of POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images

@article{
    vobecky2023POP3D,
    title={POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images},
    author={Antonin Vobecky and Oriane Siméoni and David Hurych and Spyros Gidaris and Andrei Bursuc and Patrick Pérez and Josef Sivic},
    booktitle = {Advances in Neural Information Processing Systems},
    volume = {37},
    year = {2023}
}

environment setup

Please, have GCC 5 or higher.

POP-3D

Run the following script to prepare the pop3d conda environment:

conda env create -f conda_env.yaml

Download weights from this link and put them to ./ckpts

MaskCLIP

Step 0. Create a conda environment, activate it and install requirements

cd MaskCLIP
conda create -n maskclip python=3.9
conda activate maskclip
pip install --no-cache-dir -r requirements.txt
pip install --no-cache-dir opencv-python

Step 1. Install PyTorch and Torchvision following official instructions, e.g., fo4 PyTorch 1.10 with CUDA 10.2:

pip install --no-cache-dir torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html

Step 2. Install MMCV:

pip install --no-cache-dir mmcv-full==1.5.0

Step 3. Install CLIP.

pip install ftfy regex tqdm
pip install git+https://github.com/openai/CLIP.git

Step 4. Install MaskCLIP.

pip install --no-cache-dir -v -e .

Data preparation

Download nuScenes

Download and extract the nuScenes dataset (link) and place it to the ./data/nuscenes folder. This means downloading all the nuScenes files, including both trainval and test splits,

Download "info" files:

We provide files for simpler manipulation with the nuScenes dataset. We use these files in our dataloaders. Again, please put these files to the ./data folder (in the POP3D folder). To do this, please simply run:

bash scripts/download_info_files.sh

Download retrieval benchmark files:

To download the data for our Open-vocabulary language-driven retrieval dataset, please run:

bash scripts/download_retrieval_benchmark.sh

Prepare projection files.

To activate the environment, please run:

conda activate pop3d

Run the following script to prepare projection files. The default path to the directory with the nuScenes dataset is set to ./data/nuscenes.

NUSC_ROOT=./data/nuscenes
PROJ_DIR=./data/nuscenes/features/projections
python3 generate_projections_nuscenes.py --nusc_root ${NUSC_ROOT} --proj-dir ${PROJ_DIR}

Generate MaskCLIP features.

Switch to MaskCLIP directory in this project (cd MaskCLIP).

0. Activate MaskCLIP environment:

conda activate maskclip

1. Prepare backbone weights by:

mkdir -p ./pretrain
python tools/maskclip_utils/convert_clip_weights.py --model ViT16 --backbone
python tools/maskclip_utils/convert_clip_weights.py --model ViT16

2. Download pre-trained weights from this link and put them to ckpts/maskclip_plus_vit16_deeplabv2_r101-d8_512x512_8k_coco-stuff164k.pth

3. Run feature extraction:

CFG_PATH=configs/maskclip_plus/anno_free/maskclip_plus_vit16_deeplabv2_r101-d8_512x512_8k_coco-stuff164k__nuscenes_trainvaltest.py
CKPT_PATH=ckpts/maskclip_plus_vit16_deeplabv2_r101-d8_512x512_8k_coco-stuff164k.pth
PROJ_DIR=../data/nuscenes/features/projections/data/nuscenes
OUT_DIR=../data/nuscenes/maskclip_features_projections
python tools/extract_features.py ${CFG_PATH} --save-dir ${OUT_DIR} --checkpoint ${CKPT_PATH} --projections-dir ${PROJ_DIR} --complete

to generate the target MaskCLIP+ features to use in the training of our method.

Note: the process of preparing the targets from MaskCLIP+ can be slow, depending on the speed of your file system. If you want to parallelize, we provide the following skeleton for launching multiple jobs using SLURM:

NUM_GPUS=... # fill the number of nodes
ACCOUNT=... # name of your account, if any
HOURS_TOTAL=... # how long you expect the *WHOLE* extraction of features to last
MASKCLIP_DIR=/path/to/POP3D/MaskCLIP
bash generate_features_slurm.sh ${NUM_GPUS} ${HOURS_TOTAL} ${ACCOUNT} ${MASKCLIP_DIR}

Note2: It is expected to get size mismatch for decode_head.text_embeddings: copying a param with shape torch.Size([171, 512]) from checkpoint, the shape in current model is torch.Size([28, 512]). We do not use these weights during feature extraction.

Training

Our model was trained on 8x NVIIDA A100 GPUs.

Please, modify the following variables in the training script ``:

PARTITION="..." # name of the parition on your cluser, e.g., "gpu"
ACCOUNT="..." # name of your account, if it is set on your cluster
USERNAME="..." # your username on the cluster, used just for printing of running jobs

Script to run the training using SLURM: (NOT WORKING YET)

POP3D_DIR=/path/to/POP3D
bash scripts/train_slurm.sh ${POP3D_DIR} 

Pre-trained weights

Weights used for results in the paper are here and used zero-shot weights from here. Please, put both files to ${POP3D_DIR}/pretrained folder for easier use.

Evaluation

Zero-shot open-vocabulary semantic segmentation

To obtain results from our paper, please run:

A) single-GPU (slow):

CFG=...
CKPT=...
ZEROSHOT_PTH=...
 python3 eval.py --py-config ${CFG} --resume-from ${CKPT} --maskclip --no-wandb --text-embeddings-path ${ZEROSHOT_PTH}

If you followed the instructions above, you can run:

 python3 eval.py --py-config config/pop3d_maskclip_12ep.py --resume-from ./pretrained/pop3d_weights.pth --maskclip --no-wandb --text-embeddings-path ./pretrained/zeroshot_weights.pth

B) multi-GPU using SLURM (faster), e.g.:

POP3D_DIR=`pwd`
CKPT="./pretrained/pop3d_weights.pth"
NUM_GPUS=8
HOURS=1
CFG="config/pop3d_maskclip_12ep.py"
EXTRA="--text-embeddings-path ./pretrained/zeroshot_weights.pth"
bash scripts/eval_zeroshot_slurm.sh ${POP3D_DIR} ${CKPT} ${NUM_GPUS} ${HOURS} ${CFG} ${EXTRA}

EXPECTED RESULTS:

val_miou_vox_clip_all (evaluated at the complete voxel space): 16.65827465887346

Open-vocabulary language-driven retrieval

To obtain results from our paper, please run:

python retrieval.py

Expected results:

+-------------------------------+
|       train (42 samples)      |
+----------+------+-------------+
|  method  | mAP  | mAP visible |
+----------+------+-------------+
|  POP3D   | 15.3 |     15.6    |
| MaskCLIP | N/A  |     13.5    |
+----------+------+-------------+


+-------------------------------+
|        val (27 samples)       |
+----------+------+-------------+
|  method  | mAP  | mAP visible |
+----------+------+-------------+
|  POP3D   | 24.1 |     24.7    |
| MaskCLIP | N/A  |     18.7    |
+----------+------+-------------+


+-------------------------------+
|       test (36 samples)       |
+----------+------+-------------+
|  method  | mAP  | mAP visible |
+----------+------+-------------+
|  POP3D   | 12.6 |     13.6    |
| MaskCLIP | N/A  |     12.0    |
+----------+------+-------------+


+-------------------------------+
|      valtest (63 samples)     |
+----------+------+-------------+
|  method  | mAP  | mAP visible |
+----------+------+-------------+
|  POP3D   | 17.5 |     18.4    |
| MaskCLIP | N/A  |     14.9    |
+----------+------+-------------+

Results will be written to ./results/results_${TIMESTAMP}.txt and to ./results/results_tables_${TIMESTAMP}.txt

Acknowledgements

Our code is based on TPVFormer and MaskCLIP. Many thanks to the authors!