Andreas Geiger

Andreas Geiger

@andreasgeiger.bsky.social

Professor, University of Tübingen @unituebingen.bsky.social. Head of Department of Computer Science 🎓. Faculty, Tübingen AI Center 🇩🇪 @tuebingen-ai.bsky.social. ELLIS Fellow, Founding Board Member 🇪🇺 @ellis.eu. CV 📷, ML 🧠, Self-Driving 🚗, NLP 🖺

1.7K followers819 following100 posts

Andreas Geiger reposted

Replying to @ericzzj.bsky.social

Weijie Wang@weijiewang.bsky.social

📢 We release "Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective" — a comprehensive survey covering 200+ papers on feed-forward 3D reconstruction! Instead of categorizing by 3D representations, we propose a problem-driven taxonomy. 🌐 ff3d-survey.github.io

I am so happy and excited that this project got funded!

Tübingen AI Center@tuebingen-ai.bsky.social

We are so proud of our faculty member @andreasgeiger.bsky.social for securing an ERC Consolidator Grant! With CASIDO, his team will design human-centric AI assistants to support researchers in the face of ever more rapid scientific progress. @unituebingen.bsky.social @ml4science.bsky.social

Congratulations to our PhD student @takerumiyato.bsky.social for winning the Google PhD Fellowship in the category "Machine Learning and ML Foundations". Takeru is pioneering new neural architectures that improve generalization and efficiency. Check out his research: takerum.github.io

takerum.github.ioTakeru Miyato - AI Research ScientistPhD student at University of Tübingen working on artificial intelligence, machine learning, and deep learning research.

Excited about this new work from @haoyuhe.bsky.social. TLDR: Diffusion language models treat learning and inference differently which lowers performance. RL can be used to overcome this issue for certain problems.

haoyuhe.bsky.social@haoyuhe.bsky.social

🚀 Introducing our new paper, MDPO: Overcoming the Training-Inference Divide of Masked Diffusion Language Models. 📄 Paper: www.scholar-inbox.com/papers/He202... arxiv.org/pdf/2508.13148 💻 Code: github.com/autonomousvi... 🌐 Project Page: cli212.github.io/MDPO/

A big milestone for us. Huge congrats to Bernhard, Daniel and team! The first comprehensive codebase for training RL self-driving agents with state-of-the-art performance on both CARLA 2.0 and nuPlan.

Bernhard Jaeger@bernhard-jaeger.bsky.social

We have released the code for our work, CaRL: Learning Scalable Planning Policies with Simple Rewards. The repository contains the first public code base for training RL agents with the CARLA leaderboard 2.0 and nuPlan. github.com/autonomousvi...

Thanks for being our #1 ambassador, Claire! Enjoy ICML!

Claire Vernade@claireve.bsky.social

At #ICML, you can just use scholar inbox to help you find your way through the poster sessions. It just sorts the papers according to your preferences and it really works. www.scholar-inbox.com/conference/i... ICML 2025 Planner

I am very proud of my group! These are the nationalities of my current and past team members. Diversity is key. 🇩🇪 🇬🇷 🇮🇹 🇮🇳 🇷🇺 🇺🇦 🇨🇳 🇷🇸 🇯🇵 🇧🇪 🇺🇸 🇰🇷 🇹🇷

Join us for our RealADSim Challenge! Closed-loop Driving in a 3DGS-based Simulator .. how cool is that? ;)

Yiyi Liao@yiyiliao.bsky.social

🚗 Excited to announce our RealADSim Workshop & Challenges @ ICCV 2025 — join us! 🏆 Over $40,000 in prizes 🌆 Competition 1: Extrapolated Novel View Synthesis 🚘 Competition 2: Closed-loop Driving in a 3DGS-based Simulator 🎤 Featuring great speakers 🔗 realadsim.github.io/2025/

Scholar Inbox is featured on the ML for Science blog!

ML for Science@ml4science.bsky.social

On our blog: Science is moving fast. How do we keep up? #ScholarInbox, developed by the Autonomous Vision Group led by @andreasgeiger.bsky.social, helps researchers stay ahead - by making the discovery of #openaccess papers smarter and more personal: www.machinelearningforscience.de/en/scholar-i...

Andreas Geiger reposted

The 2025 Waymo Challenge results are out! We achieved second place in the End-to-End Driving Challenge with DiffusionLTF and third place in the Scenario Generation Challenge with SHRED. Our approaches prioritized simplicity, and we're preparing to make them publicly available soon.

Andreas Geiger reposted

Excited to present our #CVPR2025 paper DepthSplat next week! DepthSplat is a feed-forward model that achieves high-quality Gaussian reconstruction and view synthesis in just 0.6 seconds. Looking forward to great conversations at the conference!

Andreas Geiger@andreasgeiger.bsky.social

🏠 Introducing DepthSplat: a framework that connects Gaussian splatting with single- and multi-view depth estimation. This enables robust depth modeling and high-quality view synthesis with state-of-the-art results on ScanNet, RealEstate10K, and DL3DV. 🔗 haofeixu.github.io/depthsplat/

Andreas Geiger reposted

🚗 Pseudo-simulation combines the efficiency of open-loop and robustness of closed-loop evaluation. It uses real data + 3D Gaussian Splatting synthetic views to assess error recovery, achieving strong correlation with closed-loop simulations while requiring 6x less compute. arxiv.org/abs/2506.04218

The PhD defense was on short notice, hence the small preliminary hat ;) Christian's work actually left a major impact with over 1400 citations within few years already. Christian was working on the forefront of real-time NVS representations.