Vincent Qin (@alpharealcat) 's Twitter Profile
Vincent Qin

@alpharealcat

⭐️Focusing on Visual Localization, SfM and SLAM.

ID: 1501017298512924674

linkhttps://github.com/Vincentqyw calendar_today08-03-2022 02:10:01

1,1K Tweet

308 Followers

371 Following

Dmytro Mishkin 🇺🇦 (@ducha_aiki) 's Twitter Profile Photo

A Guide to Structureless Visual Localization Vojtech Panek, Qunjie Zhou , Yaqing Ding, Sérgio Agostinho Zuzana Kukelova , Laura Leal-Taixe tl;dr: RoMa beats MAST3r outdoors with 5pt solver, indoors MAST3r is the king. M3Dv2 depth comparable to MAST3r 1/ arxiv.org/abs/2504.17636

A Guide to Structureless Visual Localization

Vojtech Panek, <a href="/QunjieZhou/">Qunjie Zhou</a> , Yaqing Ding, <a href="/sragostinho/">Sérgio Agostinho</a>  <a href="/ZKukelova/">Zuzana Kukelova</a>  , <a href="/lealtaixe/">Laura Leal-Taixe</a> 

tl;dr: RoMa beats MAST3r outdoors with 5pt solver, indoors MAST3r is the king.
M3Dv2 depth comparable to MAST3r
1/
arxiv.org/abs/2504.17636
MrNeRF (@janusch_patas) 's Twitter Profile Photo

FastMap: Revisiting Dense and Scalable Structure from Motion "FASTMAP, a redesigned SfM framework, achieves fast, high-accuracy dense structure from motion. On large scenes with thousands of images, FASTMAP is up to one to two orders of magnitude faster than GLOMAP and COLMAP.

Gabriele Berton (@gabriberton) 's Twitter Profile Photo

This has strong implication: it means that the layer from where you extract features is a very important hyperparameter to tune, and it can heavily improve your results And this is true regardless of the task and the model And yet, it's not something people ever tune

Deedy (@deedydas) 's Twitter Profile Photo

DeepSeek just dropped the single best end-to-end paper on large model training. It covers — Software (MLA, training in FP8, DeepEP, LogFMT) — Hardware (Multi-Rail Fat Tree, Ethernet RoCE switches) — Mix (IBGDA, 3FS filesystem) DeepSeek's engineering depth is insane. Must read.

DeepSeek just dropped the single best end-to-end paper on large model training.

It covers
— Software (MLA, training in FP8, DeepEP, LogFMT)
— Hardware (Multi-Rail Fat Tree, Ethernet RoCE switches)
— Mix (IBGDA, 3FS filesystem)

DeepSeek's engineering depth is insane. Must read.
Zhenjun Zhao (@zhenjun_zhao) 's Twitter Profile Photo

VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold Dominic Maggio, Hyungtae Lim, Luca Carlone tl;dr: VGGT->multiple submaps->projective ambiguity->submap alignment->factor graph optimization on the SL(4) manifold (Special Linear, 4 × 4 homography matrix)

VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold

Dominic Maggio, <a href="/hyungtae_lim/">Hyungtae Lim</a>, <a href="/lucacarlone1/">Luca Carlone</a>

tl;dr: VGGT-&gt;multiple submaps-&gt;projective ambiguity-&gt;submap alignment-&gt;factor graph optimization on the SL(4) manifold (Special Linear, 4 × 4 homography matrix)
Alexandre Morgand (@almorgand) 's Twitter Profile Photo

MAC-VO: Metrics-Aware Covariance for Learning-based Stereo Visual Odometry TL;DR: learning-based stereo; learned metrics-aware matching uncertainty for dual purposes: selecting keypoint and weighing the residual in pose graph optimization.