Sign in

Christoph Reich

@christophreich.bsky.social
126 followers 259 following 14 posts

@ellis.eu Ph.D. Student @CVG (@dcremers.bsky.social), @visinf.bsky.social & @oxford-vgg.bsky.social | Ph.D. Scholar @zuseschooleliza.bsky.social | M.Sc. & B.Sc. @tuda.bsky.social | Prev. @neclabsamerica.bsky.social christophreich1996.github.io

PostsRepliesMedia
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 08/09/2026
[6/6] MAGneT-3D: Monocular and Domain-Generalizable Temporal 3D Detection M. Kotb, J. Meier, @christophreich.bsky.social, O. Dhaouadi, L. Denninger, @dcremers.bsky.social Paper: arxiv.org/abs/2608.14282
arxiv.org
MAGneT-3D: Monocular and Domain-Generalizable Temporal 3D Detection
Monocular temporal 3D detection aims to detect objects in 3D, given a monocular video. Query-based 3D detectors unify detection and cross-view association, but their learnable queries fit the spatial ...
021
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 08/09/2026
[5/6] LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection J. Meier*, J. Michel*, O. Dhaouadi, Y. Yang, @christophreich.bsky.social, Z. Bauer, @stefanroth, @marcpollefeys.bsky.social, J. Kaiser, @dcremers.bsky.social 🌍 Project Page: deepscenario.github.io/LeAD-M3D
arxiv.org
LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection
Real-time monocular 3D object detection remains challenging due to severe depth ambiguity, viewpoint shifts, and the high computational cost of 3D reasoning. Existing approaches either rely on LiDAR o...
121
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 08/09/2026
[1/6] 📢 We are in Malmö at #ECCV2026 presenting 5 papers! 🎉
184
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 05/06/2026
📢 [CVPR’26] Can we learn to detect, segment, and track every object in a video without human supervision?  Yes, we introduce VideoCUPS, the first unsupervised video panoptic segmentation (VPS) method: 1. Get pseudo-labels from monocular videos. 2. Train a VPS model on them.
1154
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 04/06/2026
[1/6] 📢 We are in Denver at #CVPR2026 presenting 5 papers!
176
Reposted by Christoph Reich
Gabriele Trivigno @gabtriv.bsky.social · 07/04/2026
🔥 Can in-context segmentation emerge directly from frozen DINOv3 features? At #CVPR2026, we present INSID3: Training-Free In-Context Segmentation with DINOv3 — a collaboration between PoliTo, TU Darmstadt and TU Munich. Check it out: github.com/visinf/INSID3
1136
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 04/11/2025
📢🎓 We have open PhD positions in Computer Vision & Machine Learning at @tuda.bsky.social and @hessianai.bsky.social within the Reasonable AI Cluster of Excellence — supervised by @stefanroth.bsky.social, @simoneschaub.bsky.social and many others! www.career.tu-darmstadt.de/tu-darmstadt...
career.tu-darmstadt.de
086
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 19/10/2025
[6/8] Motion-Refined DINOSAUR for Unsupervised Multi-Object Discovery (Oral at ILR+G Workshop) by Xinrui Gong*, @olvrhhn.bsky.social *, @christophreich.bsky.social , Krishnakant Singh, @simoneschaub.bsky.social , @dcremers.bsky.social @stefanroth.bsky.social
131
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 19/10/2025
[3/8] Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion by @jev-aleks.bsky.social *, @christophreich.bsky.social *, @fwimbauer.bsky.social , @olvrhhn.bsky.social , Christian Rupprecht, @stefanroth.bsky.social, @dcremers.bsky.social 🌍 visinf.github.io/scenedino/
121
Christoph Reich @christophreich.bsky.social · 19/10/2025
Interested in 3D DINO features from a single image or unsupervised scene understanding?🦖 Come by our SceneDINO poster at NeuSLAM today 14:15 (Kamehameha II) or Tue, 15:15 (Ex. Hall I 627)! W/ Jevtić @fwimbauer.bsky.social @olvrhhn.bsky.social Rupprecht, @stefanroth.bsky.social @dcremers.bsky.social
083
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 29/09/2025
🎓 Looking for a PhD position in computer vision? Apply to the European Laboratory for Learning & Intelligent Systems (ELLIS) and work with @stefanroth.bsky.social & @simoneschaub.bsky.social! Join the info session on Oct 1. @ellis.eu @tuda.bsky.social ellis.eu/news/ellis-p...
ellis.eu
ELLIS PhD Program: Call for Applications 2025
The ELLIS mission is to create a diverse European network that promotes research excellence and advances breakthroughs in AI, as well as a pan-European PhD program to educate the next generation of AI...
0106
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 29/08/2025
Some impressions from our VISINF summer retreat at Lizumer Hütte in the Tirol Alps — including a hike up Geier Mountain and new research ideas at 2,857 m! 🇦🇹🏔️
0155
Christoph Reich @christophreich.bsky.social · 24/07/2025
Check out our blog post about SceneDINO 🦖 For more details, check out our project page, 🤗 demo, and the hashtag #ICCV2025 paper 🚀 🌍Project page: visinf.github.io/scenedino/ 🤗Demo: visinf.github.io/scenedino/ 📄Paper: arxiv.org/abs/2507.06230 @jev-aleks.bsky.social
021
Reposted by Christoph Reich
Vision and Graphics Trends @si-cv-graphics.bsky.social · 11/07/2025
𝗙𝗲𝗲𝗱-𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗦𝗰𝗲𝗻𝗲𝗗𝗜𝗡𝗢 𝗳𝗼𝗿 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗦𝗰𝗲𝗻𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 Aleksandar Jevtić, Christoph Reich, Felix Wimbauer ... Daniel Cremers arxiv.org/abs/2507.06230 Trending on www.scholar-inbox.com
0123
Reposted by Christoph Reich
Linus Härenstam-Nielsen @linushn.bsky.social · 09/07/2025
The code for our #CVPR2025 paper, PRaDA: Projective Radial Distortion Averaging, is now out! Turns out distortion calibration from multiview 2D correspondences can be fully decoupled from 3D reconstruction, greatly simplifying the problem arxiv.org/abs/2504.16499 github.com/DaniilSinits...
1125
Christoph Reich @christophreich.bsky.social · 09/07/2025
🦖 We present “Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion”. #ICCV2025 🌍: visinf.github.io/scenedino/ 📃: arxiv.org/abs/2507.06230 🤗: huggingface.co/spaces/jev-a... @jev-aleks.bsky.social @fwimbauer.bsky.social @olvrhhn.bsky.social @stefanroth.bsky.social @dcremers.bsky.social
12410
Reposted by Christoph Reich
arxiv cs.CV @arxiv-cs-cv.bsky.social · 09/07/2025
Aleksandar Jevti\'c, Christoph Reich, Felix Wimbauer, Oliver Hahn, Christian Rupprecht, Stefan Roth, Daniel Cremers Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion arxiv.org/abs/2507.06230
021
Reposted by Christoph Reich
Robin Hesse @robinhesse.bsky.social · 26/06/2025
Got a strong XAI paper rejected from ICCV? Submit it to our ICCV eXCV Workshop today—we welcome high-quality work! 🗓️ Submissions open until June 26 AoE. 📄 Got accepted to ICCV? Congrats! Consider our non-proceedings track. #ICCV2025 @iccv.bsky.social
0209
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 20/06/2025
We had a great time at #CVPR2025 in Nashville!
1202
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 11/06/2025
Scene-Centric Unsupervised Panoptic Segmentation by @olvrhhn.bsky.social , @christophreich.bsky.social , @neekans.bsky.social , @dcremers.bsky.social, Christian Rupprecht, and @stefanroth.bsky.social Sunday, 8:30 AM, ExHall D, Poster 330 Project Page: visinf.github.io/cups
172
Reposted by Christoph Reich
Dominik Schnaus @schnaus.bsky.social · 03/06/2025
Can we match vision and language representations without any supervision or paired data? Surprisingly, yes!  Our #CVPR2025 paper with @neekans.bsky.social and @dcremers.bsky.social shows that the pairwise distances in both modalities are often enough to find correspondences. ⬇️ 1/4
12712
Reposted by Christoph Reich
Felix Wimbauer @fwimbauer.bsky.social · 13/05/2025
Can you train a model for pose estimation directly on casual videos without supervision? Turns out you can! In our #CVPR2025 paper AnyCam, we directly train on YouTube videos and achieve SOTA results by using an uncertainty-based flow loss and monocular priors! ⬇️
12410
Reposted by Christoph Reich
Felix Wimbauer @fwimbauer.bsky.social · 23/04/2025
Check out our latest recent #CVPR2025 paper AnyCam, a fast method for pose estimation in casual videos! 1️⃣ Can be directly trained on casual videos without the need for 3D annotation. 2️⃣ Based around a feed-forward transformer and light-weight refinement. Code and more info: ⏩ fwmb.github.io/anycam/
1236
Christoph Reich @christophreich.bsky.social · 04/04/2025
Check out our recent #CVPR2025 #highlight paper on unsupervised panoptic segmentation🚀 🌍 visinf.github.io/cups/
080
Christoph Reich @christophreich.bsky.social · 04/04/2025
Check out the #MCML blog post on our recent #CVPR2025 #highlight paper🔥
071
Christoph Reich @christophreich.bsky.social · 13/03/2025
Check out the recent CVG papers at #CVPR2025, including our (@olvrhhn.bsky.social, @neekans.bsky.social, @dcremers.bsky.social, Christian Rupprecht, and @stefanroth.bsky.social) work on unsupervised panoptic segmentation. The paper will soon be available on arXiv. 🚀
062
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 31/01/2025
🏔️⛷️ Looking back on a fantastic week full of talks, research discussions, and skiing in the Austrian mountains!
03211
Reposted by Christoph Reich
Andreas Geiger @andreasgeiger.bsky.social · 16/01/2025
This week we had our winter retreat jointly with Daniel Cremer's group in Montafon, Austria. 46 talks, 100 Km of slopes and night sledding with some occasionally lost and found. It has been fun!
07211
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 13/12/2024
Want to learn about how model design choices affect the attribution quality of vision models? Visit our #NeurIPS2024 poster on Friday afternoon (East Exhibition Hall A-C #2910)! Paper: arxiv.org/abs/2407.11910 Code: github.com/visinf/idsds
1217
Reposted by Christoph Reich
Visual Inference Lab @visinf.bsky.social · 28/11/2024
Our work, "Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals" is accepted at TMLR! 🎉 visinf.github.io/primaps/ PriMaPs generate masks from self-supervised features, enabling to boost unsupervised semantic segmentation via stochastic EM.
1177