Junyi42/monst3r
Official Implementation of paper "MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion" observed · 2026-08-28
Health v2 · maintenance only
36/100
- Activity 27
- Release rhythm 40
- Longevity 49
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 9
- age_days: 695
- days_rel: 672
- days_push: 443
- n_releases_24m: 2
Adoption not part of the score
1386 stars · 85 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
MonST3R is the official PyTorch implementation of an ICLR 2025 paper that estimates per-timestep geometry (pointmaps) from dynamic videos in a feed-forward manner. It produces time-varying dynamic point clouds with per-frame camera poses and intrinsics, enabling downstream tasks like video depth estimation and dynamic/static scene segmentation.
Use cases
- estimate 3d geometry from dynamic videos with moving objects
- recover per-frame camera poses and intrinsics from video
- generate time-varying dynamic point clouds from footage
- estimate video depth for scenes with motion
- segment dynamic versus static parts of a scene
- visualize 4d reconstructions of dynamic scenes interactively
- run real-time feed-forward 3d reconstruction from video
When to choose
- you need geometry estimation from videos containing moving or deforming objects
- you want a feed-forward alternative to multi-stage depth-and-flow pipelines
- you need camera pose and depth outputs from monocular video for research
- you want to reproduce or build on the MonST3R paper's results
When to avoid
- you only need static-scene 3D reconstruction, where the original DUSt3R may suffice
- you need a production-ready application with a polished UI rather than research code
- you lack a GPU or cannot set up CUDA/conda environments
- you need a permissive license for commercial use, since the license is non-standard
Facets
library · maturity active
computer-vision machine-learning deep-learning image-processing data-visualization computer-vision machine-learning deep-learning artificial-intelligence graphics python cross-platform 3d-reconstruction point-cloud camera-pose-estimation depth-estimation dynamic-scenes video dust3r research-code 4d-visualization iclr-2025 linux gpu
2 sources
- readme: https://github.com/Junyi42/monst3r · fetched 2026-08-28 · 257fe18af422
- homepage: https://monst3r-project.github.io/ · fetched 2026-08-29 · 6ed1550b3880
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Junyi42/monst3r | main | 36 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem