NeRF-RPN

NeRF-RPN: A general framework for object detection in NeRFs [NeRF-RPN, CVPR 2023].

This is the official pytorch implementation of NeRF-RPN.

NeRF-RPN: A general framework for object detection in NeRFs
Benran Hu*, Junkai Huang*, Yichen Liu*, Yu-Wing Tai, Chi-Keung Tang
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

Highlights

• NeRF-RPN: The first general framework for object detection in NeRFs, which introduces RPN to NeRF for 3D object detection and related tasks.
• NeRF Dataset: The first large-scale public indoor NeRF dataset for 3D object detection, based on the existing synthetic indoor dataset Hypersim and 3D-FRONT & 3D-FUTURE, and real-world indoor dataset ScanNet, carefully curated for NeRF training.

NeRF-RPN Model Architecture

Setup

Requirements

• Ubuntu 18.04
• CUDA Version 11.1

Installation

1. Create the virtual environment:

   conda env create -f environment.yml
   conda activate nerfrpn

2. Compile CUDA extension:

   cd ./nerf_rpn/model/rotated_iou/cuda_op
   python setup.py install
   cd ../../../..

   This extension is used to calculate the intersection over union(IoU) of oriented 3D bounding boxes. Refer to Rotated_IoU for more information.

Dataset Download

Follow the instructions to download and get more information about the dataset.

Use Pretrained Model Weights

We release pertrained model weights on Hypersim, 3D-FRONT, and ScanNet NeRF datasets, using VGG19, ResNet50, and Swin-S as backbones. The models can be downloaded here.

The model names are organized in the form of {dataset name}_{method}_{backbone name}, where "anchor" refers anchor-based head and "fcos" refers to anchor-free head.

All the models are trained to predict OBB for proposals, thus enabling --rotated_bbox flag is necessary in the testing script. When using the pretrained models, please enable the --normalize_density command line flag only for models trained on 3D-FRONT.

Training from Scratch

We provide sample training shell scripts for anchor-based and anchor-free methods separately. Modify the DATA_ROOT and dataset split filename according in the scripts first.

cd ./nerf_rpn
bash train.sh           # train anchor-based model
# bash train_fcos.sh    # or train anchor-free model instead

Evaluation

Evalution scripts are also given under ./nerf_rpn. Please modify the paths as needed.

cd ./nerf_rpn
bash test.sh            # test anchor-based model
# bash test_fcos.sh     # or test anchor-free model instead

Inference on Other NeRF Datasets

For pure inference on the three datasets used in the paper, remove the --boxes_path line in the test script.

For custom datasets, you may create a csv file in the following form

,scene,rgbsigma_path,boxes_path
,custom_scene_0,/path/to/scene_0.npz,None
,custom_scene_1,/path/to/scene_1.npz,None
,custom_scene_2,/path/to/scene_2.npz,None
...

The first column contains user-defined names of the scenes. The second column are the path to corresponding extracted NeRF features. The last column can be left empty if no ground-truth is available.

Then, in the test script, set --dataset_name to general, and --test_csv to be the path to the csv file above. Also set the --output_proposals flag, so that the code will save the post-processed proposals with their confidence scores as a .npz file under the specified save path.

Additional Loss

In our test, we found that 2D projection loss and objectness loss do not help to improve our results. However, the training codes are provided for everyone to check and use.

To test 2D projection loss, you can increase the regression weight through the flag --loss_rpn_box_reg_2d

To test objectness loss, i.e., the additional RCNN object/background binary classification stage, first you need to obtain RoIs from a pretrained RPN model. We provide a sample training bash.

bash train_rcnn.sh

Citation

If you find NeRF-RPN useful in your research or refer to the provided baseline results, please star ⭐ this repository and consider citing 📝:

@inproceedings{nerfrpn,
    title = {NeRF-RPN: A general framework for object detection in NeRFs},
    author = {Hu, Benran and Huang, Junkai and Liu, Yichen and Tai, Yu-Wing and Tang, Chi-Keung},
    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2023}
}