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Novel-view-generation

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Description about the project

This project is the implementation of a novel view systhesis which aims to generate/systhesize a target view with an arbitrary camera pose from a given source view and its camera pose as shown in figure below[1].

Overarch of the proposed method

Requirements

• Python 3.x
• Open3D
• OpenCV
• Matplotlib
• Pillow
• NumPy
• MATLAB

Usage

Generating a novel view from scratch

Calibrate the camera

$ python calibrate.py

Then to generate the point cloud

$ python disparity1.py

After this, the point cloud is generated, which needs to be transformed(rotated to the target view). To do this, head over to MATLAB, and run the pcd_transformation.m
You now have the transformed point cloud of the target view.
This point cloud further needs to be projected to 2D. To do this,

$ python 3D_to_2D_open3d_part2.py

After the point cloud is rendered to 2D, a respective mask needs to be generated to perform Inpainting

$ python mask_generator.py

$ python Inpainting.py

After this stage, we now have the inpainted image, which is the target view of the given input image.

Check out some other work in novel view synthesis

• Multi-view 3D Models from Single Images with a Convolutional Network in CVPR 2016
• View Synthesis by Appearance Flow in ECCV 2016
• Transformation-Grounded Image Generation Network for Novel 3D View Synthesis in CVPR 2017
• Neural scene representation and rendering in Science 2018
• Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis in NIPS 2015
• DeepStereo: Learning to Predict New Views From the World's Imagery in CVPR 2016
• Learning-Based View Synthesis for Light Field Cameras in SIGGRAPH Asia 2016
• Novel View Synthesis in TensorFlow

References
[1] Multi-view to Novel View: Synthesizing Novel Views with Self-Learned Confidence,Sun, Shao-Hua et. al.,2018]

Authors

Nikhil A
Praveen C
Jagadish B
Vijayalaxmi P