# szymanowiczs/splatter-image

Official implementation of `Splatter Image: Ultra-Fast Single-View 3D Reconstruction' CVPR 2024

Repository: https://github.com/szymanowiczs/splatter-image
Canonical: https://ross.abutalabs.com/products/splatter-image
Homepage: https://szymanowiczs.github.io/splatter-image
Language: Python
License: BSD-3-Clause
License Family: permissive
Last push: 2024-08-17T19:39:33+00:00

## Health v2 (maintenance only)
Score: 26/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 70
- inputs: {"age_days": 989, "days_push": 746, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1106, forks 82 (observed 2026-08-28T04:03:36.464889+00:00)

## What it is
Official PyTorch implementation of 'Splatter Image: Ultra-Fast Single-View 3D Reconstruction' (CVPR 2024), which uses an image-to-image network to predict a 3D Gaussian per pixel for monocular 3D object reconstruction at 38 FPS. Includes pretrained models for multiple datasets, a Gradio demo, and multi-GPU training support.

## Use cases
- reconstruct a 3D model from a single photo
- generate 3D Gaussians from one image of an object
- render novel views of an object from a single view
- train a single-view 3D reconstruction model on Objaverse or ShapeNet
- run a fast open-category 3D reconstruction demo locally
- reconstruct video frames of an object into 3D frame-by-frame

## When to choose
- you need ultra-fast (38 FPS) feed-forward single-view 3D object reconstruction
- you want pretrained models for ShapeNet, CO3D, or Objaverse categories
- you want a research baseline with SOTA results on multi-category ShapeNet
- you have an NVIDIA GPU and want Gaussian Splatting-based monocular reconstruction

## When to avoid
- you need full scene reconstruction rather than single objects
- you have no CUDA-capable NVIDIA GPU
- you need watertight meshes rather than Gaussian splat representations
- you need a production-ready end-user application rather than research code

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, computer-vision, graphics
- domain: computer-vision, machine-learning, graphics, deep-learning
- platform: python
- tags: gaussian-splatting, 3d-reconstruction, single-view-3d, cvpr-2024, research-code, neural-networks, 3d-gaussians, gpu, linux

## Member repositories
- szymanowiczs/splatter-image (main) score 26

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:36.464889+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:44:17.461349+00:00, confidence not recorded.
  - readme: https://github.com/szymanowiczs/splatter-image (fetched 2026-08-28T04:03:36.464889+00:00, sha 0b0ef4478af1)
  - homepage: https://szymanowiczs.github.io/splatter-image (fetched 2026-08-29T12:47:58.784887+00:00, sha a59b6dc77a22)
- Data as of 2026-08-30T08:39:29.467469+00:00.