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Master thesis work on 3d object reconstruction from 2d images with graph convolution network (pixel2mesh adaption).

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markuspaschi/ShapeNetTools

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ShapeNetTools & Pixel2Mesh implementation

This repository contains some DataSet Generation and Evaluation Tools and an adapted Pixel2Mesh implementation for the following paper

Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images (ECCV2018)

Check Pixel2Mesh Repository for more information on how to set up Pixel2Mesh.

Overview

  1. Datset Downloader
  • Initial Step: Download your desired .obj files from ShapeNet or Google 3D Warehouse
  1. Dataset Tools
  • Second Step: Prepare your DataSet for Pixel2Mesh or other Neural Networks.
    • Includes the Renderer to generate png's from different viewpoints.
    • Includes Occlusion (cropping holes in png's)
    • Generating Training and Testing Split
  1. Pixel2Mesh
  • Run your desired Neural Network (in our case Pixel2Mesh) with different variants:
    • Pixel2Mesh with 2D (standard implementation)
    • Pixel2Mesh with 0.5D (only depth images)
    • Pixel2Mesh with 2.5D (rgbd images)
  1. Evalution Tools
  • Some Tools for plotting losses
  • Losses per viewpoint analysis

Dependencies

1. Requirements for Pixel2Mesh
  • Python2.7+ with Numpy and scikit-image

  • Tensorflow (version 1.0+)

  • TFLearn

  • Code has been tested with Python 2.7, TensorFlow 1.3.0, TFLearn 0.3.2, CUDA 8.0 on Ubuntu 14.04.

2. Requirements for Downloader (subject to change)
  • Python3
  • BeautifulSoup, joblib, pandas, requests, numpy
3. Requirements for Renderer
  • Python3
  • Working blender (check Renderer Readme)
  • (Meshlab)

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Master thesis work on 3d object reconstruction from 2d images with graph convolution network (pixel2mesh adaption).

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