# donydchen/mvsplat

🌊 [ECCV'24 Oral] MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images

Repository: https://github.com/donydchen/mvsplat
Canonical: https://ross.abutalabs.com/products/mvsplat
Homepage: https://donydchen.github.io/mvsplat
Language: Python
License: MIT
License Family: permissive
Topics: cost-volume, gaussian-splatting, novel-view-synthesis, feed-forward-gaussian-splatting, eccv2024
Last push: 2026-05-06T04:10:12+00:00

## Health v2 (maintenance only)
Score: 61/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 81, release rhythm 35, longevity 63
- inputs: {"age_days": 895, "days_push": 119, "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 1296, forks 84 (observed 2026-08-28T04:04:16.571928+00:00)

## What it is
MVSplat is a PyTorch implementation of an ECCV 2024 Oral model that predicts 3D Gaussians from sparse multi-view images in a single feed-forward pass using a cost volume representation. It achieves state-of-the-art novel view synthesis quality with fast inference (22 fps) and 10x fewer parameters than pixelSplat.

## Use cases
- synthesize novel views from a few posed images
- reconstruct 3D scenes as gaussian splats from sparse views
- run feed-forward 3D gaussian splatting inference
- train a multi-view stereo model on RealEstate10K or ACID
- benchmark against pixelSplat for novel view synthesis
- research feed-forward 3D reconstruction with cost volumes

## When to choose
- you need fast feed-forward novel view synthesis from sparse multi-view images
- you want a lightweight, efficient alternative to pixelSplat
- you are doing research on cost-volume-based 3D gaussian splatting

## When to avoid
- you need per-scene optimization like classic 3D Gaussian Splatting
- you lack a GPU or the training datasets (RealEstate10K/ACID)
- you need high-resolution or 360-degree output without extensions like PanSplat or MVSplat360

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, graphics
- domain: computer-vision, graphics, machine-learning, deep-learning
- platform: python
- tags: gaussian-splatting, novel-view-synthesis, cost-volume, feed-forward, 3d-reconstruction, eccv2024, research-code, gpu, linux

## Member repositories
- donydchen/mvsplat (main) score 61

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:16.571928+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 2026-08-30T04:53:56.973010+00:00, confidence not recorded.
  - readme: https://github.com/donydchen/mvsplat (fetched 2026-08-28T04:04:16.571928+00:00, sha c99f9fe1dd65)
  - homepage: https://donydchen.github.io/mvsplat (fetched 2026-08-29T12:10:33.127957+00:00, sha 8e7deb7e5b6c)
- Data as of 2026-08-30T08:39:29.467469+00:00.