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naver
mast3r
Python2024–2025
naver / mast3r

mast3r

Grounding Image Matching in 3D with MASt3R

Creative & Media
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2Distinct people
2Mentions
0Explicit recommenders
3.2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Grounding Image Matching in 3D with MASt3R

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week2 dated mentions

Observed mentions: 2024-08-27 19:00:19 UTC to 2025-01-22 22:37:38 UTC. Weeks start Monday, UTC.

012024-08-26 00:00:00 UTC to 2024-09-02 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsAug 262024-09-02 00:00:00 UTC to 2024-09-09 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-09-09 00:00:00 UTC to 2024-09-16 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-09-16 00:00:00 UTC to 2024-09-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-09-23 00:00:00 UTC to 2024-09-30 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-09-30 00:00:00 UTC to 2024-10-07 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-10-07 00:00:00 UTC to 2024-10-14 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-10-14 00:00:00 UTC to 2024-10-21 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-10-21 00:00:00 UTC to 2024-10-28 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-10-28 00:00:00 UTC to 2024-11-04 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-11-04 00:00:00 UTC to 2024-11-11 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsNov 42024-11-11 00:00:00 UTC to 2024-11-18 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-11-18 00:00:00 UTC to 2024-11-25 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-11-25 00:00:00 UTC to 2024-12-02 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-02 00:00:00 UTC to 2024-12-09 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-09 00:00:00 UTC to 2024-12-16 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-16 00:00:00 UTC to 2024-12-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-23 00:00:00 UTC to 2024-12-30 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-30 00:00:00 UTC to 2025-01-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-06 00:00:00 UTC to 2025-01-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-13 00:00:00 UTC to 2025-01-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-20 00:00:00 UTC to 2025-01-27 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJan 20

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Aug 262024-08-26 00:00:00 UTC2024-09-02 00:00:00 UTC101
Sep 22024-09-02 00:00:00 UTC2024-09-09 00:00:00 UTC000
Sep 92024-09-09 00:00:00 UTC2024-09-16 00:00:00 UTC000
Sep 162024-09-16 00:00:00 UTC2024-09-23 00:00:00 UTC000
Sep 232024-09-23 00:00:00 UTC2024-09-30 00:00:00 UTC000
Sep 302024-09-30 00:00:00 UTC2024-10-07 00:00:00 UTC000
Oct 72024-10-07 00:00:00 UTC2024-10-14 00:00:00 UTC000
Oct 142024-10-14 00:00:00 UTC2024-10-21 00:00:00 UTC000
Oct 212024-10-21 00:00:00 UTC2024-10-28 00:00:00 UTC000
Oct 282024-10-28 00:00:00 UTC2024-11-04 00:00:00 UTC000
Nov 42024-11-04 00:00:00 UTC2024-11-11 00:00:00 UTC000
Nov 112024-11-11 00:00:00 UTC2024-11-18 00:00:00 UTC000
Nov 182024-11-18 00:00:00 UTC2024-11-25 00:00:00 UTC000
Nov 252024-11-25 00:00:00 UTC2024-12-02 00:00:00 UTC000
Dec 22024-12-02 00:00:00 UTC2024-12-09 00:00:00 UTC000
Dec 92024-12-09 00:00:00 UTC2024-12-16 00:00:00 UTC000
Dec 162024-12-16 00:00:00 UTC2024-12-23 00:00:00 UTC000
Dec 232024-12-23 00:00:00 UTC2024-12-30 00:00:00 UTC000
Dec 302024-12-30 00:00:00 UTC2025-01-06 00:00:00 UTC000
Jan 62025-01-06 00:00:00 UTC2025-01-13 00:00:00 UTC000
Jan 132025-01-13 00:00:00 UTC2025-01-20 00:00:00 UTC000
Jan 202025-01-20 00:00:00 UTC2025-01-27 00:00:00 UTC101

What Hacker News says

2 sources
ryandamm·Jan 22, 2025
You could try generating per-view depth maps, going to a point cloud and meshing from there. (I suspect splats may reduce your accuracy as an intermediate.) I’m not aware of a fully-baked workflow for that — though it may exist. The first step has gotten really good: the recent single-shot AI models for depth are pretty visually impressive (I don’t know about metric accuracy). The ones I’m aware of are DUST3R and the newer MAST3R: https://github.com/naver/dust3r https://github.com/naver/mast3r Good luck!
In Hunyuan3D 2.0 – High-Resolution 3D Assets Generation
refibrillator·Aug 27, 2024
Novel view synthesis via 3DGS requires knowledge of the camera pose for every input image, ie the cam position and orientation in 3D space. Historically camera poses have been estimated via 2D image matching techniques like SIFT [1], through software packages like COLMAP. These algorithms work well when you have many images that methodically cover a scene. However they often struggle to produce accurate estimates in the few image regime, or “in the wild” where photos are taken casually with less rigorous scene coverage. To address this, a major trend in the field is to move away from classical 2D algorithms, instead leveraging methods that incorporate 3D “priors” or knowledge of the world. To that end, this paper builds heavily on MASt3R [2], which is a vision transformer model that has been trained to reconstruct a 3D scene from 2D image
Read the rest
pairs. The authors added another projection head to output the initial parameters for each gaussian primitive. They further optimize the gaussians through some clever use of the original image pair and randomly selected and rendered novel views, which is basically the original 3DGS algorithm but using synthesized target images instead (hence “zero-shot” in the title). I do think this general approach will dominate the field in the coming years, but it brings its own unique challenges. In particular, the quadratic time complexity of transformers is the main computational bottleneck preventing this technique from being scaled up to more than two images at a time, and to resolutions beyond 512 x 512. Also, naive image matching itself has quadratic time complexity, which is really painful with large dense latent vectors and can’t be accelerated with kd-trees due to the curse of dimensionality. That’s why the authors use a hierarchical coarse to fine algorithm that approximates the exact computation and achieves linear time complexity wrt to image resolution. [1] https://en.m.wikipedia.org/wiki/Scale-invariant_feature_tran... [2] https://github.com/naver/mast3r
In Splatt3R: Zero-Shot Gaussian Splatting from Uncalibrated Image Pairs

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