Roof Classification, Segmentation, and Damage Completion using 3D Point Clouds

This repository contains the project for the course: https://dvl.in.tum.de/teaching/adl4cv-ws18/

About

We design a Deep Learning framework that directly consumes unordered point sets as inputs, representing the Roof of a building. A Point Cloud is represented as a set of 3D points {Pi| i = 1, ..., n}, where each point Pi is a vector of its (x, y, z) coordinate. We perform the following tasks:

1. Classification: Clasifying the input point cloud into 3 categories: Saddleback Roof, Pyramid Roof, and Two-Sided Hip Roof
2. Segmentation: Classifiying each point into semantic sub-categories.
3. Damaging: Manually simulating damages of various shapes in the roofs.
4. Completion: Completing the damaged point cloud
5. Multi-Task Learning: Perform Classification, and Segmentation in a multi-task setting for original, and perturbed dataset.

Approach

1. PointNet [2] is used for Classification, Segmentation and Multi-Task Learning on original as well as perturbed dataset
2. Point Completion Network [3] is used for completing the damaged roofs.
3. The damage has been manually simulated on the roof point clouds.
4. Data has been provided by [1].

Usage

Demo

1. Clone the repository: git clone https://github.com/sarthakTUM/roofn3d.git
2. go to cd roofn3d. Install the requirements: pip install -r requirements.txt. It is recommended to perform this step in a separate virtual environment. If using Conda, then conda create --name roofn3d.
3. For classification, segmentation, and multi-task demo: cd cls_seg_mt.
4. Run python demo.py. Different examples can be seen by changing the --idxparameter. For example, python demo.py --idx=15. The --idx parameter can be a maximum of 23.
5. For Roof Completion, go to cd completion from the roof directory.
6. Run python demo.py. Different example can be seen by changing the --input_path parameter. For example: python demo.py --input_path=demo_data/saddleback_roof.pcd, or python demo.py --input_path=demo_data/twosidedhip_roof.pcd.
7. Demo data for both the tasks have been added to demo:data directories.

The models are cloned along with the repository. If there are any difficulties in cloning the models, please download the models from:

1. https://drive.google.com/open?id=1C8X4O9SnNmvmJbzpqx_2YJR7sYTxZ3Tm for Classification, segmentation, and Multi-Task networks. Place the models in the roofn3d/cls_seg_mt/models directory.
2. https://drive.google.com/open?id=15r54fXLjZcFuL2ok35M1_hXxw_Fn7LJu for Completion. Place the model in completion/log directory.

Training

Classification, Segmentation, and Multi-Task

Non-Damaged Data: download the data from https://drive.google.com/open?id=1hYBrNl2nficT_rFJkFUlXQYjR-2QvNQF Damaged data: download the data from https://drive.google.com/open?id=1gNqYKxKP3CT_Fd8D_L6OWk545tAR4Oxl

1. Unzip it into a folder.

2. go to cd cls_seg_mt. Run python train_classification.py --input_path=path_to_data_from_step2 --outf=models/cls. You must change the --input-pathto path of data dobtained from step 2. The outf argument corresponds to output drectory for the trained models. For example: python train_classification.py --input_path=data/roofn3d_data_multitask_all --outf=models/cls.

3. for segmentation, run python train_segmentation.py --input_path=path_to_data_from_step2 --outf=models/seg. You must change the --input-pathto path of data dobtained from step 2. The outf argument corresponds to output drectory for the trained models. For example: python train_segmentation.py --input_path=data/roofn3d_data_multitask_all --outf=models/seg.

4. For Multi-Task Learning, run python train_multitask.py --input_path=path_to_data_from_step2 --outf=models/mt. You must change the --input-pathto path of data dobtained from step 2. The outf argument corresponds to output drectory for the trained models. For example: python train_multitask.py --input_path=data/roofn3d_data_multitask_all --outf=models/mt.

NOTE: The first run might take some time to load all the point-clouds in the memory and save them for faster access in the next run. It is recommended to spare atleast 10GB of RAM for data loading.

Testing

1. go to cd cls_seg_mt, and run python test_cls.py --input_path=path_to_data --model=model_to_test.pth. For example: python test_cls.py --input_path=data/roofn3d_data_multitask_all --model=models/classification_complete.pth.

2. go to cd cls_seg_mt, and run python test_seg.py --input_path=path_to_data --model=model_to_test.pth. For example: python test_seg.py --input_path=data/roofn3d_data_multitask_all --model=models/segmentation_complete.pth.

3. go to cd cls_seg_mt, and run python test_multitask.py --input_path=path_to_data --model=model_to_test.pth. For example: python test_multitask.py --input_path=data/roofn3d_data_multitask_all --model=models/multitask_complete.pth.

Acknowledgement

The RoofN3D training data (Wichmann et al., 2018) was provided by the chair Methods of Geoinformation Science of Technische Universität Berlin and is available at https://roofn3d.gis.tu-berlin.de.

Citations

[1] Wichmann, A., Agoub, A., Kada, M., 2018. RoofN3D: Deep Learning Training Data for 3D Building Reconstruction. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2, pp. 1191-1198.

[2] Qi Charles, R & Su, Hao & Kaichun, Mo & Guibas, Leonidas. (2017). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. 77-85. 10.1109/CVPR.2017.16.

[3] Yuan, Wentao & Khot, Tejas & Held, David & Mertz, Christoph & Hebert, Martial. (2018). PCN: Point Completion Network. 728-737. 10.1109/3DV.2018.00088.