DeepSDF

Simple implementation of the paper DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. with the skyscrapers data. Leveraging the Deep Signed Distance Functions model with latent vectors, this project aims to build the algorithm that can synthesize infinite number of skyscrapers similar to trained data. The detailed process for this project is archived here.

Synthesized data with deepSDF model

Installation

This repository uses the image named nvcr.io/nvidia/pytorch:23.10-py3 for running devcontainer.

1. Ensure you have Docker and Visual Studio Code with the Remote - Containers extension installed.

2. Clone the repository.

       git clone https://github.com/PARKCHEOLHEE-lab/deep-sdf.git
   

3. Open the project with VSCode.

4. When prompted at the bottom left on the VSCode, click Reopen in Container or use the command palette (F1) and select Remote-Containers: Reopen in Container.

5. VS Code will build the Docker container and set up the environment.

6. Once the container is built and running, you're ready to start working with the project.

File Details

data

• raw-skyscrapers: The directory containing the raw data for skyscrapers.
• preprocessed-skyscrapers: The directory containing preprocessed skyscrapers for training.
• preprocessed-skyscrapers-dynamic-sampled: The directory containing dynamically sampled data by the number of raw data' vertices. (deprectaed)

src

• config.py: Configurations related to the model and data.
• data_creator.py: Creates the data consisting of xyz coordinates and sdf values.
• model.py: Defines the classes for SDFdatset, SDFdecoder, SDFdecoderTrainer.
• reconstruct.py: Defines a class to reconstruct skyscrapers with skimage.measure.marching_cubes.
• synthesize.py: Defines Synthesizer for creating synthesized data.
• utils.py: Utility functions not related to the model.

notebooks

• deep_sdf.ipynb: Execute the whole processes for the training and testing.

runs

• 2024-03-24_12-40-26: The directory containing the model's states, reconstruction results.
• 2024-03-24_12-40-26/states/all_states.pth: Pre-trained model states.