PlaneRecon

Plane-Based RGB-D reconstruction of indoor scenes with geometry and texture optimization.

An example of plane partition result of scan copyroom from BundleFusion dataset:

Textured mesh:

Re-rendered RGB and depth sequence on textured mesh:

Related publications

Please cite these two papers if you want to use the code and data:

@inproceedings{wang2018plane,
  title={[Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes]},
  author={Wang, Chao and Guo, Xiaohu},
  booktitle={2018 International Conference on 3D Vision (3DV)},
  pages={533--541},
  year={2018},
  organization={IEEE}
}

(here is PDF) and

@InProceedings{Wang_2019_CVPR_Workshops,
author = {Wang, Chao and Guo, Xiaohu},
title = {Efficient Plane-Based Optimization of Geometry and Texture for Indoor RGB-D Reconstruction},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}

(here is PDF)

Usage

PlaneRecon pipeline contains 4 programs running in time order:

• mesh_partition: takes as input a dense mesh, partition and then simplify it based on planes, and output simplified mesh with partition.
• mesh_visibility: takes as input one mesh and camera poses in some RGB-D frames, compute the visible mesh vertices in each frame;
• blur_estimation: estimate image blurriness for color images from a RGB-D sequence;
• mesh_texture_opt: takes as input: 1) RGB-D sequence, including color and depth images and camera poses; 2) the simplified mesh from mesh_partition; 3) visibility data across frames from mesh_visibility; 4) blurriness of color images from blur_estimation. Output: final textured obj mesh with optimized geometry and texture.

You can use the script run_linux.sh to run the entire pipeline. Note to modify relevant input parameters.

Each code has its own ReadMe file about usage and compilation. Refer to them for more details.

Dependencies

Dependencies for all programs are:

• Eigen (matrix computation)
• OpenCV 2 or 3 (image processing and I/O);
• gflags (global flags and debug);
• GLEW (OpenGL support, only needed in mesh_visibility code)
• GLFW (window and interface, only needed in mesh_visibility code)
• GLM (OpenGL math, code already included, only needed in mesh_visibility code)

Build

In linux, simply run build_linux.sh and it will build all 4 programs.

Will support Windows build soon.

Data

A typical input data for this code can be found from BundleFusion or 3DLite data, which contains reconstructed PLY model and camera pose files for each RGB-D sequence.

This code also supports ICL-NUIM dataset, but the sequence format is slightly different. You need to change it to fit the BundleFusion data format. Refer to run_linux.sh for more details. Also, if you want to create a dense mesh from ICL-NUIM data or other RGB-D sequence, you can try VoxelHashing.

Result

The folder models contains result textured meshes used in the paper.

Note

• Currently this code is a CPU-only. Actually OpenMP or GPU computation can be introduced to many time-consuming processes in the code. Will try to accelerate the code soon.
• It's not hard to combine these programs into one program. Actually, this can also save some data I/O time. We use separate programs just for better debugging and easier reusing.
• mesh_partition program needs large amount of memory. For instance, for a mesh with 1M faces, it takes about 20G memory.

License

MIT License. See the LICENSE file for details.

Other relevant code

Plane detection on RGB-D frames

Source code can be found here: https://github.com/chaowang15/RGBDPlaneDetection