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https://3dlg-hcvc.github.io/mirror3d/
https://3dlg-hcvc.github.io/mirror3d/
Last synced: 2 months ago
JSON representation
- Host: GitHub
- URL: https://3dlg-hcvc.github.io/mirror3d/
- Owner: 3dlg-hcvc
- License: mit
- Created: 2020-11-20T10:44:10.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-10-12T19:38:14.000Z (over 1 year ago)
- Last Synced: 2024-01-22T21:18:29.321Z (5 months ago)
- Language: Python
- Size: 8.33 MB
- Stars: 38
- Watchers: 6
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-rgbd-datasets - Mirror3D Dataset - similar to Microsoft Kinect v1 |Mirror (Other) |Indoor |Color, Depth |Mirror Mask |7011 scenes with mirror |2021 | (RGB-D Datasets <a id="list" class="anchor" href="#list" aria-hidden="true"><span class="octicon octicon-link"></span></a>)
README
# [Mirror3D: Depth Refinement for Mirror Surfaces](https://3dlg-hcvc.github.io/mirror3d)
[Jiaqi Tan](https://christinatan0704.github.io/mysite/), [Weijie Lin](https://lewislinn.github.io/), [Angel X. Chang](https://github.com/angelxuanchang)
, [Manolis Savva](https://github.com/msavva)
## Preparation for all implementations
```shell
mkdir workspace && cd workspace
### Put data under dataset folder
mkdir dataset
### Clone this repo and pull all submodules
git clone --recursive https://github.com/3dlg-hcvc/mirror3d.git
```
## Environment Setup
- python 3.7.4
```shell
### Install packages
cd mirror3d && pip install -e .
### Setup Detectron2
python -m pip install git+https://github.com/facebookresearch/detectron2.git
```
## Dataset
Please refer to [Mirror3D Dataset](docs/Mirror3D_dataset.md) for instructions on how to prepare mirror data. Please visit our [project website](https://3dlg-hcvc.github.io/mirror3d) for updates and to browse more data.
Matterport3D
ScanNet
NYUv2
### Mirror annotation tool
Please refer to [User Instruction](docs/user_instruction.md) for instructions on how to annotate mirror data.
## Models
### Mirror3DNet PyTorch Implementation
Mirror3DNet architecture can be used for either an RGB image or an RGBD image input. For an RGB input, we refine the depth of the predicted depth map Dpred output by a depth estimation module. For RGBD input, we refine a noisy input depth Dnoisy.
Please check [Mirror3DNet](https://github.com/3dlg-hcvc/mirror3d/tree/main/mirror3d/mirror3dnet) for our network's pytorch implementation.
### Initial Depth Generator Implementation
We test three methods on our dataset:
- [BTS: From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation](https://github.com/cogaplex-bts/bts)
- [VNL: Enforcing geometric constraints of virtual normal for depth prediction](https://github.com/YvanYin/VNL_Monocular_Depth_Prediction)
- [saic : Decoder Modulation for Indoor Depth Completion](https://github.com/saic-vul/saic_depth_completion)
We updated the dataloader and the main train/test script in the original repository to support our input format.
## Network input
Our network inputs are JSON files stored based on [coco annotation format](https://cocodataset.org/#home). Please download [network input json](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/mirror3d_input.zip) to train and test our models.
## Training
Please remember to prepare the mirror data according to [Mirror3D Dataset](docs/Mirror3D_dataset.md) before training and inference.
To train our models please run:
```shell
cd workspace
### Download network input json
wget http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/mirror3d_input.zip
unzip mirror3d_input.zip
### Get R-50.pkl from detectron2 to train Mirror3DNet and PlaneRCNN
mkdir checkpoint && cd checkpoint
wget https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl
cd ../mirror3d
### Train on NYUv2 mirror data
bash script/nyu_train.sh
### Train on Matterport3D mirror data
bash script/mp3d_train.sh
```
By default, we put the unzipped data and network input packages under `../dataset`. Please change the relevant configuration if you store the data in different directories. Output checkpoints and tensorboard log files are saved under `--log_directory`.
## Inference
```shell
### Download all pretrained checkpoints
cd workspace
wget http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint.zip
unzip checkpoint.zip
### Download network input json
wget http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/mirror3d_input.zip
unzip mirror3d_input.zip
cd mirror3d
### Inference on NYUv2 mirror data
bash script/nyu_infer.sh
### Inference on Matterport3D mirror data
bash script/mp3d_infer.sh
```
Output depth maps are saved under a folder named `pred_depth`. Optional: If you want to view all inference results on an html webpage, please run all steps in [mirror3d/visualization/result_visualization.py](https://github.com/3dlg-hcvc/mirror3d/blob/main/mirror3d/visualization/result_visualization.py).
## Pretrained checkpoint
Individual checkpoint included in the `checkpoint.zip` above. Please use `wget` command to download to the .zip file if there's no response clicking the link.
| **Source Dataset** | **Input** | **Train** | Method | **Model Download** |
|--------------------|-----------|----------------------|---------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|
| NYUv2 | RGBD | raw sensor depth | [saic](https://github.com/saic-vul/saic_depth_completion/tree/94bececdf12bb9867ce52c970bb2d11dee948d37) | [saic_rawD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/nyu/saic_rawD.zip) |
| NYUv2 | RGBD | refined sensor depth | [saic](https://github.com/saic-vul/saic_depth_completion/tree/94bececdf12bb9867ce52c970bb2d11dee948d37) | [saic_refD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/nyu/saic_refD.zip) |
| NYUv2 | RGB | raw sensor depth | [BTS](https://github.com/cogaplex-bts/bts) | [bts_nyu_v2_pytorch_densenet161.zip](https://cogaplex-bts.s3.ap-northeast-2.amazonaws.com/bts_nyu_v2_pytorch_densenet161.zip) |
| NYUv2 | RGB | refined sensor depth | [BTS](https://github.com/cogaplex-bts/bts) | [bts_refD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/nyu/bts_refD.zip) |
| NYUv2 | RGB | raw sensor depth | [VNL](https://github.com/YvanYin/VNL_Monocular_Depth_Prediction) | [nyu_rawdata.pth](https://cloudstor.aarnet.edu.au/plus/s/7kdsKYchLdTi53p) |
| NYUv2 | RGB | refined sensor depth | [VNL](https://github.com/YvanYin/VNL_Monocular_Depth_Prediction) | [vnl_refD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/nyu/vnl_refD.zip) |
| Matterport3D | RGBD | raw mesh depth | [Mirror3DNet](https://github.com/3dlg-hcvc/mirror3d/tree/main/mirror3dnet) | [mirror3dnet_rawD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/mp3d/mirror3dnet_rawD.zip) |
| Matterport3D | RGBD | refined mesh depth | [Mirror3DNet](https://github.com/3dlg-hcvc/mirror3d/tree/main/mirror3dnet) | [mirror3dnet_refD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/mp3d/mirror3dnet_refD.zip) |
| Matterport3D | RGBD | raw mesh depth | [PlaneRCNN](https://github.com/NVlabs/planercnn/tree/01e03fe5a97b7afc4c5c4c3090ddc9da41c071bd) | [planercnn_rawD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/mp3d/planercnn_rawD.zip) |
| Matterport3D | RGBD | refined mesh depth | [PlaneRCNN](https://github.com/NVlabs/planercnn/tree/01e03fe5a97b7afc4c5c4c3090ddc9da41c071bd) | [planercnn_refD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/mp3d/planercnn_refD.zip) |
| Matterport3D | RGBD | raw mesh depth | [saic](https://github.com/saic-vul/saic_depth_completion/tree/94bececdf12bb9867ce52c970bb2d11dee948d37) | [saic_rawD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/mp3d/saic_rawD.zip) |
| Matterport3D | RGBD | refined mesh depth | [saic](https://github.com/saic-vul/saic_depth_completion/tree/94bececdf12bb9867ce52c970bb2d11dee948d37) | [saic_refD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/mp3d/saic_refD.zip) |
| Matterport3D | RGB | * | [Mirror3DNet](https://github.com/3dlg-hcvc/mirror3d/tree/main/mirror3dnet) | [mirror3dnet.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/mp3d/mirror3dnet_normal_10.zip) |
| Matterport3D | RGB | raw mesh depth | [BTS](https://github.com/cogaplex-bts/bts) | [bts_rawD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/mp3d/bts_rawD.zip) |
| Matterport3D | RGB | refined mesh depth | [BTS](https://github.com/cogaplex-bts/bts) | [bts_refD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/mp3d/bts_refD.zip) |
| Matterport3D | RGB | raw mesh depth | [VNL](https://github.com/YvanYin/VNL_Monocular_Depth_Prediction) | [vnl_rawD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/mp3d/vnl_rawD.zip) |
| Matterport3D | RGB | refined mesh depth | [VNL](https://github.com/YvanYin/VNL_Monocular_Depth_Prediction) | [vnl_refD.zip](http://aspis.cmpt.sfu.ca/projects/mirrors/mirror3d_zip_release/checkpoint/mp3d/vnl_refD.zip) |