git《Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction》(ECCV 2020) GitHub:

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Deep Learning interplane
Overview

Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction

Code for the ECCV 2020 paper by Yiming Qian and Yasutaka Furukawa

Getting Started

Clone the repository:

git clone https://github.com/yi-ming-qian/interplane.git

We use Python 3.7 and PyTorch 1.0.0 in our implementation, please install dependencies:

conda create -n interplane python=3.7
conda activate interplane
conda install pytorch==1.0.0 torchvision==0.2.1 cuda90 -c pytorch
conda install -c menpo opencv
pip install -r requirements.txt

Dataset

We create our pairwise plane relationship dataset based on PlaneRCNN. Please follow the instructions in their repo to download their dataset.

Then dowload our relationship dataset from here, and do the following: (1) merge the "scans/" folder with "$ROOT_FOLDER/scans/", (2) place "contact_split/" under "$ROOT_FOLDER/", (3) place "planeae_result" under "$ROOT_FOLDER/".

Training

We have three networks, Orientation-CNN, Contact-CNN, Segmentation-MPN, which are trained separately:

python train_angle.py train with dataset.dataFolder=$ROOT_FOLDER/
python train_contact.py train with dataset.dataFolder=$ROOT_FOLDER/
python train_segmentation.py train with dataset.dataFolder=$ROOT_FOLDER/

Evaluation

Evaluate when input method is PlaneRCNN:

python predict_all.py eval with dataset.dataFolder=$ROOT_FOLDER/ resume_angle=/path/to/orientationCNN/model  resume_contact=/path/to/contactCNN/model resume_seg=/path/to/segmentationMPN/model input_method=planercnn

Evaluate when input method is PlaneAE:

python predict_all.py eval with dataset.dataFolder=$ROOT_FOLDER/ resume_angle=/path/to/orientationCNN/model  resume_contact=/path/to/contactCNN/model resume_seg=/path/to/segmentationMPN/model input_method=planeae

Two gpus are used for inference. The results will be saved under "experiments/predict/{RUN_ID}/results/". We also provide our pre-trained models here.

Contact

https://yi-ming-qian.github.io/

Acknowledgements

We thank the authors of PlaneRCNN and of PlaneAE. Our implementation is heavily built upon their codes.

Comments
  • Questions in datasets/plane_predict_dataset.py & Where to get '$ROOT_FOLDER/scans/scene%04d_%02d/scene%04d_%02d.txt'

    Questions in datasets/plane_predict_dataset.py & Where to get '$ROOT_FOLDER/scans/scene%04d_%02d/scene%04d_%02d.txt'

    Hi, when I used predict_all.py to evaluate the result, surprisingly I found that no result has been returned. I was certain that I followed up your instructions to download the dateset of PlaneRCNN and merged your relation_data.zip,so I checked for a long time how it was going?

    Finally I found my question in 'datasets/plane_predict_dataset.py'. In the "init" of 'class PlaneDataset'. When loading '/ScanNet/Tasks/Benchmark/scannetv1_test.txt' in line 35, many of lines in this txt were not contained in the variable 'planenet_scene_ids_val' in line 33 and even if some of them really existed, I sill stuck in the condition "if not os.path.exists(scenePath + '/' + scene_id + '.txt') or not os.path.exists(scenePath + '/annotation/planes.npy'):" in line 44. Therefore no scene could be successfully loaded. Why could it lead such result?

    Besides,after I ignored that part of code , I still failed due to the lack of '$ROOT_FOLDER/scans/scene%04d_%02d/scene%04d_%02d.txt'.However, I thought myself following your instructions strictly, so I wondered where to get those txts.

    Thanks beforehand.

    opened by Darkgleeman 6
  • Ground-truth plane segmentation from LabelMe release

    Ground-truth plane segmentation from LabelMe release

    Hi Thanks for your work!

    I notice in a previous issue you mentioned that you manually combine some of the isolated masks to further improve the gt mask quality. Could you please release the updated gt annotation files?

    opened by jcliu0428 4
  • How to generate 3D model from the output of refine segmentations

    How to generate 3D model from the output of refine segmentations

    Hi @qymwill @yi-ming-qian

    I have got results from this command from nvlabs/planercnn repo python evaluate.py --methods=f --suffix=warping_refine --dataset=inference --customDataFolder=example_images

    snap_results

    How should i use it to generate 3D mesh ?

    https://github.com/NVlabs/planercnn/issues/53 . I know this is not correct place to create issue but i didnt get reply from anyone who has worked in it previously that y i have created issue here. Do you know any method with above outputs to generate 3dmesh output .ply file which is like in paper

    opened by ajithvallabai 4
  • Ground-truth plane segmentation annotation in Section 5.2

    Ground-truth plane segmentation annotation in Section 5.2

    Thanks for your great work ! In Section 5.2, you say the segmentation labels in PLaneRCNN have large errors and you annotate ground-truth by LabelMe tool. Could you provide the annotated plane segmentation labels ?

    opened by weiyithu 2
  • Bump pyyaml from 5.1.2 to 5.4

    Bump pyyaml from 5.1.2 to 5.4

    Bumps pyyaml from 5.1.2 to 5.4.

    Changelog

    Sourced from pyyaml's changelog.

    5.4 (2021-01-19)

    5.3.1 (2020-03-18)

    • yaml/pyyaml#386 -- Prevents arbitrary code execution during python/object/new constructor

    5.3 (2020-01-06)

    5.2 (2019-12-02)

    • Repair incompatibilities introduced with 5.1. The default Loader was changed, but several methods like add_constructor still used the old default yaml/pyyaml#279 -- A more flexible fix for custom tag constructors yaml/pyyaml#287 -- Change default loader for yaml.add_constructor yaml/pyyaml#305 -- Change default loader for add_implicit_resolver, add_path_resolver
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    opened by dependabot[bot] 1
  • Bump pillow from 6.2.0 to 8.1.1

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    8.1.1 (2021-03-01)

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    • Fix OOB Read in TiffDecode.c, and check the tile validity before reading. CVE-2021-25291 [wiredfool]

    • Fix negative size read in TiffDecode.c. CVE-2021-25290 [wiredfool]

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  • Bump tensorflow from 1.14.0 to 1.15.2

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    TensorFlow 1.15.2

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    ... (truncated)
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    Release 1.15.2

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    Breaking Changes

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    • tf.config.experimentalVirtualDeviceConfiguration has been renamed to tf.config.LogicalDeviceConfiguration.
    • tf.config.experimental_list_devices has been removed, please use tf.config.list_logical_devices.

    Bug Fixes and Other Changes

    ... (truncated)
    Commits
    • 5d80e1e Merge pull request #36215 from tensorflow-jenkins/version-numbers-1.15.2-8214
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    dependencies 
    opened by dependabot[bot] 1
  • custom dataset

    custom dataset

    I know that in all works PlaneRCNN, PlaneAE use scan dataset, but the question always arises how to get the parameter "plane" and no one can give a clear answer. For example we have: image, segmentation, camera parameters, depth. But never the parameter "plane". For example how to teach a stanford2d-3d Semantics Dataset How to create custom dataset ?

    opened by KirillHiddleston 0
  • Only 100 images for testing?

    Only 100 images for testing?

    Hi, I have a question in your work and the former PlaneRCNN. In your code and PlaneRCNN:

    https://github.com/yi-ming-qian/interplane/blob/f0713e4d97c03221b29769b2679eb69031469312/predict_all.py#L110

    Only 100 images are tested. I wonder is this true?

    opened by lijing1996 0
  • RGB image or RGB-D for inference ?

    RGB image or RGB-D for inference ?

    Hello

    Can we use simple RGB images taken by mobile phones in prediction ? The model as you know trained by RGB-D images. If it is possible to predict RGB images with your pretrained model, would you please guide which parameters to include ?

    Thanks

    opened by Fizmath 0
  • How to predict results for a single image?

    How to predict results for a single image?

    Hi,

    I was trying to get the results on a single image but it seems the predict_all.py uses the complete dataset for the evaluation. The repo doesn't have a prediction function for a single image like PlaneAE. Can you tell me are you planning to work on that or suggest ways so I can implement it myself.

    Thanks beforehand.

    opened by thevoxium 1
Owner
null
GitHub
A Planar RGB-D SLAM which utilizes Manhattan World structure to provide optimal camera pose trajectory while also providing a sparse reconstruction containing points, lines and planes, and a dense surfel-based reconstruction.

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