Neural ICP Fields for 3D Human Registration at Scale

Riccardo Marin · Enric Corona . Gerard Pons-Moll

[arXiv]

This repository contains the inference code for NSR registration pipeline. The code has been tested on Linux Ubuntu 20.04.6 LTS, using python 3.8.13, and a GPU GeForce RTX 3080 Ti.

Getting Started

1. Clone the repo

git clone https://github.com/riccardomarin/NICP.git NICP
cd NICP

2. Create the environment

conda create -n nsr python=3.8.13
conda activate nsr

3. Run the installation script (it also contains checkpoint download)

./install.sh

4. You need to download the smplh model and place it in the support_data folder. The correct file structure is:

support_data
  |__ body_models
         |__smplh
             |__neutral
                 |__model.npz

5. Set the home directory path in ./src/lvd_templ/paths.py

home_dir                          = #e.g., '/home/ubuntu/Documents/NICP/'

You are ready to start!

Inference

To use NSR and fit all the scans into the demo folder, you can run the following command:

PYTHONPATH=. python ./src/lvd_templ/evaluation/evaluation_benchmark.py

We also provide a streamlit demo to run INLoVD on single shapes using a GUI.

PYTHONPATH=. streamlit run ./src/lvd_templ/evaluation/stream_demo.py

Change Settings in evaluation_benchmark.py

Depending on your use case, you may specify different paremeters for the INLoVD pipeline. This can be easily done my command line. For example:

PYTHONPATH=. python ./src/lvd_templ/evaluation/evaluation_benchmark.py core.cham_bidir=-1

will run a unidirectional chamfer distance refinement (-1: input with oultiers; 1: Partial input).

If you want to characterize a run (and avoid overwrite), you can specify a tag:

PYTHONPATH=. python ./src/lvd_templ/evaluation/evaluation_benchmark.py core.tag='greedy' core.lr_ss=0.01

A complete list of available parameters can be found in conf_test/default.yaml.

TODO

• Inference code for the checkpoint trained on AMASS
• Training code
• Code to replicate quantitative evaluations
• Checkpoints for LoVD and LVD trained on CAPE