JohnRomanelis/SPVD_Lightning
SPVD⚡: Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models (PyTorch Lightning)
This project helps researchers and engineers create detailed 3D models (point clouds) from sparse information. You input a sparse set of 3D points, and it outputs a complete, high-resolution 3D point cloud of an object or scene. This is useful for anyone working with 3D data generation, computer graphics, or robotics.
No commits in the last 6 months.
Use this if you need to generate realistic and detailed 3D point cloud models from limited or incomplete 3D sensor data.
Not ideal if you are looking for a pre-trained model for immediate use without any programming or deep learning setup.
Stars
8
Forks
2
Language
Python
License
—
Category
Last pushed
Sep 13, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/JohnRomanelis/SPVD_Lightning"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
PRIS-CV/DemoFusion
Let us democratise high-resolution generation! (CVPR 2024)
mit-han-lab/distrifuser
[CVPR 2024 Highlight] DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models
Tencent-Hunyuan/HunyuanPortrait
[CVPR-2025] The official code of HunyuanPortrait: Implicit Condition Control for Enhanced...
giuvecchio/matfuse
MatFuse: Controllable Material Generation with Diffusion Models (CVPR2024)
Shilin-LU/TF-ICON
[ICCV 2023] "TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition" (Official...