Laura Leal-Taixe
@lealtaixe
Senior Research Manager at @NVIDIA. Prev Professor at @TU_Muenchen. Computer Vision mostly. Views are my own.
Excited to share what we've been working on! SeNaTra introduces a backbone where segmentation emerges natively by replacing standard downsampling with grouping layers. Opens the door for a new family of zero-shot segmentation-centric backbone architectures! 🚀 Code coming soon!
The time for new architectures is over? Not quite! SeNaTra, a native segmentation backbone, is waiting, let's see how it works 🧵arxiv.org/abs/2505.16993
Can we learn to complete anything in Lidar without any manual supervision? Excited to share our #ICML2025 paper “Towards Learning to Complete Anything in Lidar” from my time at @nvidia with @CristianoSalto @NeeharPeri @meinhardt_tim @RdeLutio @AljosaOsep @lealtaixe! Thread🧵👇
Curious about 3D Gaussians, simulation, rendering and the latest from #NVIDIA? Come to the NVIDIA Kaolin Library live-coding session at #CVPR2025, powered by a cloud GPU reserved especially for you. Wed, Jun 11, 8-noon. Bring your laptop! tinyurl.com/nv-kaolin-cvpr…
Turns out that if you learn to downsample (rather than using uniform grid pooling) in Vision Transformers, you no longer need dedicated upsampling layers and segmentation heads—dense image segmentation emerges natively. arxiv.org/abs/2505.16993
The time for new architectures is over? Not quite! SeNaTra, a native segmentation backbone, is waiting, let's see how it works 🧵arxiv.org/abs/2505.16993
A Guide to Structureless Visual Localization Vojtech Panek, Qunjie Zhou, Yaqing Ding, Sérgio Agostinho, @ZKukelova, @SattlerTorsten, @lealtaixe tl;dr: structureless localization review arxiv.org/abs/2504.17636
Nvidia just announced Towards Learning to Complete Anything in Lidar
Thanks @_akhaliq for sharing! During my internship at @NVIDIAAI, we explored zero-shot panoptic completion of Lidar scans — together with @CristianoSalto @NeeharPeri @meinhardt_tim @RdeLutio @lealtaixe @AljosaOsep!
Nvidia just announced Towards Learning to Complete Anything in Lidar
Spatial AI is increasingly important, and the newest papers from #NVIDIAResearch, 3DGRT and 3DGUT, represent significant advancements in enabling researchers and developers to explore and innovate with 3D Gaussian Splatting techniques. 💎 3DGRT (Gaussian Ray Tracing) ➡️…
MATCHA:Towards Matching Anything @FeiXue94, @s_elflein, @lealtaixe, @QunjieZhou tl;dr: diffusion model->semantic+geometric features->transformer-based fusion->enhanced diffusion features->w/ DINOv2->unified feature->geometric/semantic/temporal matching arxiv.org/abs/2501.14945
If you want to try out our 3DV paper #DynOMo for dynamic, online, monocular reconstruction-based point tracking, you can do so now ☺️💃 @lealtaixe @QunjieZhou @BDuisterhof @RamananDeva jennyseidenschwarz.github.io/DynOMo.github.…
To appear at 3DV! Congrats to the team, especially @JennySeidensch1 !
You wondered how point tracks generated from dynamic, online, monocular reconstruction look in action? Enjoy the sneak peak of #DynOMo on TAPVID-Davis, PanopticSports and the iPhone dataset! More visuals soon 🚀@lealtaixe @QunjieZhou @BDuisterhof @RamananDeva
My group at NVIDIA is looking for interns for next year! If you are excited about research in dynamic scene understanding, perception & reconstruction, camera pose estimation, or even climate modelling, apply using the link below or contact me directly! nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAEx…