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PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos

Project Page | Paper

PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos
Yiming Xie, Matheus Gadelha, Fengting Yang, Xiaowei Zhou, Huaizu Jiang
CVPR 2022

How to Use

Installation

conda env create -f environment.yaml
conda activate planarrecon

Follow instructions in torchsparse to install torchsparse.

Pretrained Model on ScanNet

Download the pretrained weights and put it under PROJECT_PATH/checkpoints/release. You can also use gdown to download it in command line:

gdown --id 1XLL5X2M5BPo89An4jom5s0zhQOiyS_h8

Data Preperation for ScanNet

Download and extract ScanNet by following the instructions provided at http://www.scan-net.org/.

You can obtain the train/val/test split information from here.

DATAROOT
└───scannet
│   └───scans
│   |   └───scene0000_00
│   |       └───color
│   |       │   │   0.jpg
│   |       │   │   1.jpg
│   |       │   │   ...
│   |       │   ...
│   └───scans_raw
│   |   └───scene0000_00
│   |       └───scene0000_00.aggregation.json
│   |       └───scene0000_00_vh_clean_2.labels.ply
│   |       └───scene0000_00_vh_clean_2.0.010000.segs.json
│   |       │   ...
|   └───scannetv2_test.txt
|   └───scannetv2_train.txt
|   └───scannetv2_val.txt
|   └───scannetv2-labels.combined.tsv

Next run the data preparation script which parses the raw data format into the processed pickle format. This script also generates the ground truth Planes. The plane generation code is modified from PlaneRCNN.

# Change PATH_TO_SCANNET accordingly.
# For the training/val split:
python tools/generate_gt.py --data_path PATH_TO_SCANNET --save_name planes_9/ --window_size 9 --n_proc 2 --n_gpu 1

Inference on ScanNet val-set

python main.py --cfg ./config/test.yaml

The planes will be saved to PROJECT_PATH/results.

Evaluation on ScanNet val-set

Evaluate 3D geometry:

python tools/eval3d_geo_ins.py --model ./results/scene_scannet_release_68 --n_proc 16

Evaluate plane segmentation:

# generate gt instance txt
python tools/prepare_inst_gt_txt.py --val_list path_to_scannetv2_val.txt --plane_mesh_path path_to_planes_tsdf_9

# eval instance
python tools/eval3d_instance.py --pred_path path_to_pred/plane_ins --gt_path path_to_planes_tsdf_9/instance --scan_list path_to_scannetv2_val.txt

Training on ScanNet

Similar to NeuralRecon, the training is seperated to three phases and the switching is controlled manually for now:

• Phase 0 (the first 0-20 epoch), training single fragments.
  MODEL.FUSION.FUSION_ON=False, MODEL.TRACKING=False

#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0,1
python -m torch.distributed.launch --nproc_per_node=2 main.py --cfg ./config/train_phase0.yaml

• Phase 1 (21-35 epoch), with GRUFusion.
  MODEL.FUSION.FUSION_ON=True, MODEL.TRACKING=False

#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0,1
python -m torch.distributed.launch --nproc_per_node=2 main.py --cfg ./config/train_phase1.yaml

• Phase 2 (the remaining 35-50 epoch), with Matching/Fusion.
  MODEL.FUSION.FUSION_ON=True, MODEL.TRACKING=True

#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0,1
python -m torch.distributed.launch --nproc_per_node=2 main.py --cfg ./config/train_phase2.yaml

Real-time Demo on Custom Data with Camera Poses from ARKit.

We provide a demo of PlanarRecon running with self-captured ARKit data. Please refer to DEMO.md for details. We also provide the example data captured using iPhoneXR. Incrementally saving and visualizing are not enabled in PlanarRecon for now.

Citation

If you find this code useful for your research, please use the following BibTeX entry.

@article{xie2022planarrecon,
  title={{PlanarRecon}: Real-Time {3D} Plane Detection and Reconstruction from Posed Monocular Videos},
  author={Xie, Yiming and Gadelha, Matheus and Yang, Fengting and Zhou, Xiaowei and Jiang, Huaizu},
  journal={CVPR},
  year={2022}
}

Acknowledgment

Some of the code and installation guide in this repo is borrowed from NeuralRecon! We also thank Atlas for the 3D geometry evaluation.