# dcharatan/pixelsplat

[CVPR 2024 Oral, Best Paper Runner-Up] Code for "pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction" by David Charatan, Sizhe Lester Li, Andrea Tagliasacchi, and Vincent Sitzmann

Repository: https://github.com/dcharatan/pixelsplat
Canonical: https://ross.abutalabs.com/products/pixelsplat
Homepage: http://davidcharatan.com/pixelsplat/
Language: Python
License: MIT
License Family: permissive
Last push: 2025-01-13T15:36:14+00:00

## Health v2 (maintenance only)
Score: 27/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 1, release rhythm 35, longevity 70
- inputs: {"age_days": 987, "days_push": 597, "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 1274, forks 79 (observed 2026-08-28T04:04:12.723968+00:00)

## What it is
pixelSplat is a PyTorch implementation of a feed-forward model that reconstructs 3D radiance fields parameterized by 3D Gaussian primitives from pairs of images in a single forward pass. It enables real-time, memory-efficient novel view synthesis and fast 3D reconstruction, benchmarked on RealEstate10k and ACID datasets.

## Use cases
- reconstruct 3D scenes from image pairs
- novel view synthesis from sparse views
- generate 3D Gaussian splats from images
- render depth maps from reconstructed Gaussians
- research on generalizable 3D reconstruction
- train models on RealEstate10k or ACID datasets

## When to choose
- you need fast feed-forward 3D reconstruction from two or more images
- you want real-time rendering with 3D Gaussian splatting
- you are doing research on generalizable novel view synthesis
- you need an interpretable, editable 3D radiance field output

## When to avoid
- you need per-scene optimization with maximum quality rather than speed
- you lack a CUDA-capable GPU with sufficient memory
- you need reconstruction from a single image
- you want a production-ready application rather than research code

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning, image-processing, graphics, computer-vision
- domain: computer-vision, deep-learning, machine-learning, graphics
- platform: python
- tags: 3d-reconstruction, gaussian-splatting, novel-view-synthesis, feed-forward-model, cvpr-2024, research-code, pytorch, linux, gpu

## Member repositories
- dcharatan/pixelsplat (main) score 27

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:12.723968+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 2026-08-30T05:03:06.871405+00:00, confidence not recorded.
  - readme: https://github.com/dcharatan/pixelsplat (fetched 2026-08-28T04:04:12.723968+00:00, sha 3c7bf080145f)
  - homepage: http://davidcharatan.com/pixelsplat/ (fetched 2026-08-29T12:14:29.033151+00:00, sha 752c2855a1ac)
- Data as of 2026-08-30T08:39:29.467469+00:00.