Supercharge Your 3D Data: A Deep Dive into SynCD Dataset Generation

Are you ready to unlock the potential of 3D data with the SynCD dataset? This guide provides a comprehensive walkthrough, empowering you to generate your own tailored datasets for deformable and rigid objects. Revolutionize your 3D modeling and computer vision projects by diving into this invaluable resource.

Download the Pre-Generated SynCD Dataset

Jumpstart your projects! You can immediately leverage a pre-filtered version of the SynCD dataset, offering a ready-to-use resource for various 3D applications.

Setting Up Your Environment for SynCD Dataset Generation

Generating your custom SynCD dataset requires a robust setup. Here's what you need:

• Hardware: A GPU with at least 48GB VRAM is essential for handling the computational demands.
• Base Environment: Configure your environment by following the instructions provided with the SynCD dataset.

Generating Deformable Datasets with SynCD

Create datasets that accurately represent real-world object flexibility and movement.

1. Navigate to the dataset directory: cd dataset
2. Execute the generation script: python gen_deformable.py --save_attn_mask --outdir assets/metadata/deformable_data

This command generates a deformable dataset, saving attention masks and outputting the results to the specified directory (assets/metadata/deformable_data).

Benefit: Generate realistic training data to improve the performance of your deformable object models, vital for applications like robotics and animation.

Generating Rigid Datasets with SynCD: A Step-by-Step Guide

Produce datasets focused on non-deformable objects using these instructions.

Single Objaverse Asset Generation:

1. Download Pre-Generated Prompts: Download prompts using wget https://www.cs.cmu.edu/~syncd-project/assets/prompts_objaverse.pt -P assets/generated_prompts/
2. Unzip Rendering Assets: Use the provided bash script to unzip the Objaverse rendering assets: bash assets/unzip.sh assets/metadata/objaverse_rendering/
3. Run the Generation Script: Launch the dataset generation process. This command uses torchrun to distribute the workload, enhancing efficiency.: torchrun --nnodes=1 --nproc_per_node=1 --node_rank=0 --master_port=12356 gen_rigid.py --rootdir ./assets/metadata --promptpath assets/generated_prompts/prompts_objaverse.pt --outdir assets/metadata/rigid_data

Benefit: Craft precise training data for rigid object recognition and manipulation, ideal for manufacturing and object tracking systems.

Objaverse Guided Rigid Dataset Generation:

This method leverages Objaverse, a universe of 3D objects, to enrich your dataset. This approach uses FLUX.1-Depth-dev for depth conditioning -- a departure from the original paper.

1. Install Dependencies: Before beginning, ensure you have the necessary packages.

   pip install objaverse
   pip install ninja
   pip install trimesh
   pip install "git+https://github.com/facebookresearch/pytorch3d.git"
   

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2. Download and Unzip Rendering Subsets: Download a subset of Objaverse renderings:

   cd assets/metadata/objaverse_rendering
   wget https://huggingface.co/datasets/nupurkmr9/objaverse_rendering/resolve/main/archive_1.zip
   unzip archive_1.zip
   cd ../../../
   bash assets/unzip.sh assets/metadata/objaverse_rendering/
   

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3. Calculate Multi-View Correspondence: Generate correspondence data: python gen_corresp.py --download --rendered_path ./assets/metadata/objaverse_rendering --objaverse_path ./assets/metadata/objaverse_assets --outdir assets/metadata

4. Dataset Generation: Generate the final dataset: torchrun --nnodes=1 --nproc_per_node=1 --node_rank=0 --master_port=12356 gen_rigid.py --rootdir ./assets/metadata --promptpath assets/generated_prompts/prompts_objaverse.pt --outdir <output-path-to-save-dataset>

Key Insight: By using Objaverse, you can access a vast library of 3D models, enabling richer and more diverse datasets.

Generate Prompts from LLM for Custom Categories

Customize your SynCD dataset further by generating prompts using Large Language Models (LLMs).

1. Download Background Descriptions: Retrieve background descriptions for Objaverse assets: wget https://huggingface.co/datasets/tiange/Cap3D/resolve/main/misc/Cap3D_automated_Objaverse_old.csv?download=true -O Cap3D_automated_Objaverse_old.csv

2. Run the Prompt Generation Script: Generate prompts based on your desired categories. python gen_prompts.py --rigid --captions Cap3D_automated_Objaverse_old.csv

Benefit: Create targeted prompts tailored to your specific object categories, enhancing the relevance and utility of your dataset. You can generate prompts for your own categories for maximum flexibility.

Unlock the Power of SynCD for Your 3D Projects

With this comprehensive guide, you're equipped to generate custom SynCD datasets that cater to your specific needs. Whether you're working with deformable or rigid objects, SynCD provides the tools to create high-quality training data, boosting the accuracy and efficiency of your 3D modeling and computer vision applications. Start building your ideal 3D dataset today!