README.md

Autoencoders with Hyper and Target Networks for Compact Representations of 3D Point Clouds

Authors: Przemysław Spurek, Sebastian Winczowski, Jacek Tabor, Maciej Zamorski, Maciej Zįeba, Tomasz Trzcínski

arXiv
Hypernetwork approach to generating point clouds (abs)
Hypernetwork approach to generating point clouds (pdf)

ICML 2020
Hypernetwork approach to generating point clouds (abs)
Hypernetwork approach to generating point clouds (pdf)

Abstract

In this work, we propose a novel method for generating 3D point clouds that leverage properties of hyper networks. Contrary to the existing methods that learn only the representation of a 3D object,our approach simultaneously finds a representation of the object and its 3D surface. The main idea of our HyperCloud method is to build a hyper network that returns weights of a particular neural network (target network) trained to map points from a uniform unit ball distribution into a 3D shape. As a consequence, a particular 3D shape can be generated using point-by-point sampling from the assumed prior distribution and transform-ing sampled points with the target network. Since the hyper network is based on an auto-encoder architecture trained to reconstruct realistic 3D shapes, the target network weights can be considered a parametrization of the surface of a 3D shape, and not a standard representation of point cloud usually returned by competitive approaches.The proposed architecture allows finding mesh-based representation of 3D objects in a generative manner while providing point clouds en pair in quality with the state-of-the-art methods.

Requirements

• dependencies stored in requirements.txt.
• Python 3.6+
• cuda

Installation

If you are using Conda:

• run ./install_requirements.sh

otherwise:

• install cudatoolkit and run pip install -r requirements.txt

Then execute:

export CUDA_HOME=... # e.g. /var/lib/cuda-10.0/
./build_losses.sh

Configuration (settings/hyperparams.json, settings/experiments.json):

• arch -> aae | vae
• target_network_input:normalization:type -> progressive
• target_network_input:normalization:epoch -> epoch for which the progressive normalization, of the points from uniform distribution, ends
• reconstruction_loss -> chamfer | earth_mover
• dataset -> shapenet

Frequency of saving training data (settings/hyperparams.json)

"save_weights_frequency": int (> 0) -> save model's weights every x epochs
"save_samples_frequency": int (> 0) -> save intermediate reconstructions every x epochs

Target Network input

Uniform distribution:

3D points are sampled from uniform distribution.

Normalization

When normalization is enabled, points are normalized progressively from first epoch to target_network_input:normalization:epoch epoch specified in the configuration.

As a result, for epochs >= target_network_input:normalization:epoch, target network input is sampled from a uniform unit 3D ball

Exemplary config:

"target_network_input": {
    "constant": false,
    "normalization": {
        "enable": true,
        "type": "progressive",
        "epoch": 100
    }
}
For epochs: [1, 100] target network input is normalized progressively
For epochs: [100, inf] target network input is sampled from a uniform unit 3D ball

Usage

Add project root directory to PYTHONPATH

export PYTHONPATH=project_path:$PYTHONPATH

Training

python experiments/train_[aae|vae].py --config settings/hyperparams.json

Results will be saved in the directory: ${results_root}/[aae|vae]/training/uniform*/${dataset}/${classes}

Experiments

python experiments/experiments.py --config settings/experiments.json

Results will be saved in the directory: ${results_root}/[aae|vae]/experiments/uniform*/${dataset}/${classes}

Model weights are loaded from path:

• ${weights_path} if specified
• otherwise: ${results_root}/${arch}/training/.../weights (make sure that target_network_input and classes are the same in the hyperparams.json/experiments.json)

Sphere distribution:

The following experiments provide input of the target network as samples from a triangulation on a unit 3D sphere:

• sphere_triangles
• sphere_triangles_interpolation

3D points are sampled uniformly from the triangulation on a unit 3D sphere.

Available methods: hybrid | hybrid2 | hybrid3 | midpoint | midpoint2 | centroid | edge

Compute metrics

python experiments/compute_metrics.py --config settings/experiments.json

Model weights are loaded from path:

• ${weights_path} if specified
• otherwise: ${results_root}/${arch}/training/.../weights (make sure that target_network_input and classes are the same in the hyperparams.json/experiments.json)

Shapenet dataset classes

Classes can be specified in the hyperparams/experiments file in the classes key

airplane,  bag,        basket,     bathtub,   bed,        bench, 
bicycle,   birdhouse,  bookshelf,  bottle,    bowl,       bus,      
cabinet,   can,        camera,     cap,       car,        chair,    
clock,     dishwasher, monitor,    table,     telephone,  tin_can,  
tower,     train,      keyboard,   earphone,  faucet,     file,     
guitar,    helmet,     jar,        knife,     lamp,       laptop,   
speaker,   mailbox,    microphone, microwave, motorcycle, mug,      
piano,     pillow,     pistol,     pot,       printer,    remote_control,      
rifle,     rocket,     skateboard, sofa,      stove,      vessel,   
washer,    boat,       cellphone

License

This implementation is licensed under the MIT License