RIM-Net

Paper | Video | Slides

RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structures
Chengjie Niu, Manyi Li, Kevin (Kai) Xu, Hao (Richard) Zhang.

Results

RIM-Net recursively decomposes an input 3D shape into two parts, resulting in a binary tree hierarchy. Each level of the tree corresponds to an assembly of shape parts, represented as implicit functions, to reconstruct the input shape.

Getting Started

Environment

Python3.6.0, TensorFlow 1.9.0, CUDA 11.2.

Dataset

We use the ready-to-use dataset provided by BAE-Net, the link is:
https://drive.google.com/file/d/1NvbGIC-XqZGs9pz6wgFwwEPALR-iR8E0/view

Pre-trained models

The link of pre-trained models is:
https://drive.google.com/drive/folders/1OJSuks0fQ-plFrPH2EuDX58aMAt_pQz8?usp=sharing

Usage:

1. Change the correct path for 'dataset' and 'checkpoint' in RIM_main.py

2. Set 'train' to 'True' in RIM_main.py for training.

3. Set 'train' to 'False', and 'recon' to 'True' in RIM_mian.py for testing.

If you have more questions, please contact nchengjie@gmail.com.