valeo.ai @valeoai
We are a research team on artificial intelligence for automotive applications working toward assisted and autonomous driving. valeoai.github.io Paris, France Joined December 2019-
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If you're coming to @eccvconf consider our freshly accepted tutorial on: A Bayesian Odyssey in Uncertainty: from Theoretical Foundations 📝 to Real-World Applications 🚀 w/ the amazing @GianniFranchi10 @_olivierlaurent @a1mmer @Pavel_Izmailov More info coming soon #eccv2024
Today we had with us the stellar @GDrettakis with a historical perspective of rendering from traditional to neural up to their Gaussian Splatting. He sprinkled lots of pointers to older ideas that made it work eventually and inspiration on crafting and pursuing a research idea
Always happy to see self-supervised learning going beyond ImageNet and, well, images 🙃 @Coro_Sautier et al. propose BEVContrast: a clever pretraining strategy for automotive Lidar with good performance in several downstream tasks in both low-shot and fully labeled settings 👇
Always happy to see self-supervised learning going beyond ImageNet and, well, images 🙃 @Coro_Sautier et al. propose BEVContrast: a clever pretraining strategy for automotive Lidar with good performance in several downstream tasks in both low-shot and fully labeled settings 👇
Domain adaptation for LiDAR data is hard with varying lidar patterns and acquisition conditions. Models struggle to maintain performance consistency across domains 🥵 If you are attending #3DV2024, check SALUDA a solution for unsupervisd domain adaptation for LiDAR data!
Domain adaptation for LiDAR data is hard with varying lidar patterns and acquisition conditions. Models struggle to maintain performance consistency across domains 🥵 If you are attending #3DV2024, check SALUDA a solution for unsupervisd domain adaptation for LiDAR data!
Happy to present the following works at #3DV2024 this week👇
@captnloic @Bjoern_Michele @AlexandreBoulch @gillespuy @nicolas_courty BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds. Corentin Sautier, @gillespuy, @AlexandreBoulch, @VincentLepetit2, Renaud Marlet. pdf: arxiv.org/pdf/2310.17281… code: github.com/valeoai/BEVCon…
@captnloic SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation. @Bjoern_Michele, @AlexandreBoulch, @gillespuy, Tuan-Hung Vu, Renaud Marlet, @nicolas_courty pdf: arxiv.org/pdf/2304.03251… code: github.com/valeoai/SALUDA
Our friend Suha Kwak 🇰🇷 stopped by to talk about his work on Robust Visual Perception in Adverse Conditions (distribution shift, challenging weather, etc.) Check out the great work of his team in this area 👉 suhakwak.github.io
Our @valeoai squad at #FOMOWinterSchool. Thanks @ELLISforEurope @Ellis_Amsterdam for this amazing opportunity to meet all these inspiring PhD students and researchers!
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4K Followers 201 Following Biggest AI industrial research organization in France. #AI, #robotics, #computervision, #machinelearning, #deeplearning, #NLProc, #UXIf you're coming to @eccvconf consider our freshly accepted tutorial on: A Bayesian Odyssey in Uncertainty: from Theoretical Foundations 📝 to Real-World Applications 🚀 w/ the amazing @GianniFranchi10 @_olivierlaurent @a1mmer @Pavel_Izmailov More info coming soon #eccv2024
🚨@CIIRCCTU researchers @AVobecky and @JosefSivic have developed a new #POP3D method, which was a big success at the @NeurIPSConf 2023. The work was developed in collaboration with the @valeoai research team, & it's part of the EXA4MIND Industrial Application Case. Read more⤵️
🚨 Výzkumníci CIIRC @AVobecky a @JosefSivic vyvinuli novou metodu #POP3D, která zaznamenala velký úspěch i na konferenci @NeurIPS 2023. Práce vznikla ve spolupráci s výzkumným týmem @valeoai. Přečtěte si více zde: 1url.cz/iu6nU @EXA4MIND
🚨 Výzkumníci CIIRC @AVobecky a @JosefSivic vyvinuli novou metodu #POP3D, která zaznamenala velký úspěch i na konferenci @NeurIPS 2023. Práce vznikla ve spolupráci s výzkumným týmem @valeoai. Přečtěte si více zde: 1url.cz/iu6nU @EXA4MIND
Check out 👉PointBeV👈, our new model for BEV semantic segmentation. With its sparse approach, it is: 🎯SotA 🧠memory efficient 🤹highly flexible to various scenarios @LoickCh will present it at #CVPR24 in Seattle
✨ PointBeV brings the *sparse* paradigm to BEV segmentation #CVPR24 Why sparse? 🧠 Memory efficiency *AND* SotA performance 🤹♂️ Tailored for flexibility at inference time. Long or short range? Coarse or high resolution? LiDAR or HD maps? PointBeV adapts to the situation! ⬇️[1/6]
Always happy to see self-supervised learning going beyond ImageNet and, well, images 🙃 @Coro_Sautier et al. propose BEVContrast: a clever pretraining strategy for automotive Lidar with good performance in several downstream tasks in both low-shot and fully labeled settings 👇
Meet BEVContrast: a surprisingly simple self-supervised learning (SSL) method for automotive Lidar point clouds, with no pre-processing, consistent hyperparams across datasets and architectures, and strong performance boosts in both low-shot and full label regimes #3DV2024 🧵 1/
Pushing forward adaptation of 3D semantic segmentation models from one Lidar scanner (32 beams) to another (40-64 beams) without labels. Check out SALUDA at #3DV2024 by @Bjoern_Michele et al.
Interested in unsupervised domain adaptation for LiDAR data ? Then, I am happy to see you at the spotlight presentation of our paper “SALUDA” this afternoon at 15:00 or at the poster session @3DVconf. Paper: arxiv.org/abs/2304.03251 Code: github.com/valeoai/SALUDA
Interested in unsupervised domain adaptation for LiDAR data ? Then, I am happy to see you at the spotlight presentation of our paper “SALUDA” this afternoon at 15:00 or at the poster session @3DVconf. Paper: arxiv.org/abs/2304.03251 Code: github.com/valeoai/SALUDA
Happy to present the following works at #3DV2024 this week👇
@captnloic @Bjoern_Michele @AlexandreBoulch @gillespuy @nicolas_courty BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds. Corentin Sautier, @gillespuy, @AlexandreBoulch, @VincentLepetit2, Renaud Marlet. pdf: arxiv.org/pdf/2310.17281… code: github.com/valeoai/BEVCon…
@captnloic SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation. @Bjoern_Michele, @AlexandreBoulch, @gillespuy, Tuan-Hung Vu, Renaud Marlet, @nicolas_courty pdf: arxiv.org/pdf/2304.03251… code: github.com/valeoai/SALUDA
Prof. Suha Kwak from @postech2020 gives a talk on robustness against adverse conditions at our regular scientific seminar at @valeoai.
@VLetzelter is presenting his very first work at #Neurips2023 ! It was motivated by sound source localization, but it provides insights on more general Winner-Takes-All regression objectives!
@mtmthh @valeoai @ELLISforEurope @Ellis_Amsterdam Glad to have all here!!
@mtmthh @valeoai @ELLISforEurope It’s been a pleasure to have this cool squad at our winter school!
Don't miss this very interesting thread from our colleague @AVobecky, explaining the latest developments that @valeoai and @CIIRCCTU have made for our ⚙️industrial application case, which aims to develop #AI methods and models for 🌐🚗Advanced Driver Assistance Systems. ⤵️⤵️
[@NeurIPSConf'23]🚨Did you miss it? Our POP-3D generates open-vocabulary 3D occupancy predictions from 📷 surround-view images only, w/o human labels & w/ distillation from pre-trained models. We also propose a new small 3D-occupancy open-vocabulary benchmark #neurips2023⬇️ [1/N]
[@NeurIPSConf'23]🚨Did you miss it? Our POP-3D generates open-vocabulary 3D occupancy predictions from 📷 surround-view images only, w/o human labels & w/ distillation from pre-trained models. We also propose a new small 3D-occupancy open-vocabulary benchmark #neurips2023⬇️ [1/N]
Interestingly, the POP-3D logo design turned out quite visionary for its time 🙃
Last December @AVobecky presented our POP-3D model which generates 3D occupancy with open-vocabulary representation from images only, trained w/o annotation. We also proposed a new small benchmark for open-voc. 3D-occupancy w/ natural language queries. More details below ⬇️
[@NeurIPSConf'23]🚨Did you miss it? Our POP-3D generates open-vocabulary 3D occupancy predictions from 📷 surround-view images only, w/o human labels & w/ distillation from pre-trained models. We also propose a new small 3D-occupancy open-vocabulary benchmark #neurips2023⬇️ [1/N]
Lifting 2D pixels from a rig of cameras on a vehicle into 3D voxels w/ open-vocabulary features is now possible w/ POP-3D, designed by @AVobecky et al. Check out our approach for distilling pre-trained vision-language models and lidar info for outdoor surround-view perception 👇
[@NeurIPSConf'23]🚨Did you miss it? Our POP-3D generates open-vocabulary 3D occupancy predictions from 📷 surround-view images only, w/o human labels & w/ distillation from pre-trained models. We also propose a new small 3D-occupancy open-vocabulary benchmark #neurips2023⬇️ [1/N]
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