📢 Benchmark for 6D Object Pose Estimation 📢 BOP challenge 24 has been opened! https://t.co/Od5U0LGzS9 Results to be presented at the R6D workshop at #ECCV2024 Details in comments below👇

10th (!!) R6D workshop @ ICCV 2025: https://t.co/bFx9WxizlG 🤖 Object pose estimation for industrial robotics 📢 Strong speakers: @svlevine, @haoshu_fang, @MaxlDur , @GusKalra , @vaheta 📊 BOP Challenge 2025 with new BOP-Industrial datasets With @ma_sundermeyer

Universal Beta Splatting Contributions: • Universal Beta Splatting: A unified N-dimensional representation with per-dimension shape control, enabling simultaneous modeling of spatial, angular, and temporal properties through anisotropic Beta kernels with spatial-orthogonal

Universal Beta Splatting Rong Liu, @ZhongpaiGao, Benjamin Planche, @MeidaC, @vannguyen_ng, Meng Zheng, @AnwesaChoudhuri, Terrence Chen, @yuewang314, Andrew Feng, Ziyan Wu tl;dr: radiance field rendering->N-dimensional anisotropic Beta kernels https://t.co/HsBAkhjf7v

⚠️Reconstructing sharp 3D meshes from a few unposed images is a hard and ambiguous problem. ☑️With MAtCha, we leverage a pretrained depth model to recover sharp meshes from sparse views including both foreground and background, within mins!🧵 🌐Webpage: https://t.co/di9e52XqFb

Not long until the 9th(!) Workshop on Recovering 6D Object Pose (R6D) at #ECCV2024, Sunday AM. Great speakers, and @vannguyen_ng, @tomhodan and @ma_sundermeyer will tell us about the #BOP Challenge 24 - the challenge is still running, but you get to see early bird results!

📈 BOP update: Deadline for the 2024 challenge is extended to November 29! BOP'24 focuses on model-free and model-based 2D/6D object detection, and introduces three new datasets from Meta and NVIDIA – HOT3D, HOPEv2, and HANDAL. https://t.co/5nWv125raN

#CVPR2024 poster presentation starting now at poster 358 🌟

Starting now at poster 233 🌟

Introducing, HOT3D. HOT3D is a new dataset from our team at Meta Reality Labs Research, to explore vision-based methods for hand-object interaction. https://t.co/aDg0rNSDSq 1/

We are releasing 🔥HOT3D🔥, a new egocentric dataset for 3D hand and object tracking. 833 minutes (3.7M images) of multi-view image streams showing 19 subjects interacting with 33 objects, annotated with high-quality 3D poses of hands and objects. Paper:

#CVPR2024 Thu 20 (AM) GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence @vannguyen_ng @thibaultgroueix Mathieu Salzmann @VincentLepetit2 pdf: https://t.co/lP22K1uUkj webpage: https://t.co/FoutSRgjE1 code: https://t.co/iK19JiIOYc

#CVPR2024 Thu 20 (PM) NOPE: Novel Object Pose Estimation from a Single Image @vannguyen_ng @thibaultgroueix Georgy Ponimatkin,   Yinlin Hu,  Renaud Marlet,  Mathieu Salzmann @VincentLepetit2 pdf: https://t.co/rMCU4zAccg web: https://t.co/RElHEaXXDq code: https://t.co/FQDemkDoM9

#CVPR Fri 21 (AM) OpenStreetView-5M: The Many Roads to Global Visual Geolocation @g_astruc @nico_dufour @YSiglidis @Elt_Vincent @RomainLoiseau15 @xkungfu @vannguyen_ng @captnloic + others pdf: https://t.co/0gwVnlbKSQ web: https://t.co/47K5S6M7xV

The new BOP challenge 2024 just opened! 🔊 This year we are also competing on end-to-end *model-free* 6D object pose estimation! 🌟 After 5min/1GPU with an onboarding video of the target object, estimate the pose of the object in cluttered scenes.

BOP is back with #ECCV2024. The presentation of the #BOP2023 results is just around the corner (at the CV4MR workshop, #CVPR2024), but we have no time to loose. Time for #BOP2024. Thought pose estimation of unseen objects w/ a 3D model was hard? Try it based on a dynamic video!

Besides, we also introduce three new datasets which enable evaluation of the model-based, and model-free tasks (all these datasets include both CAD models and onboarding videos): HOT3D captured from Aria glasses and Quest 3 from #Meta, HOPEv2 and HANDAL from #NVIDIA

Dynamic onboarding: The object is manipulated by hands and the camera is either static (on a tripod) or dynamic (on a head-mounted device). Object masks for all video frames and the 6D object pose for the first frame are available.

We define two types of reference videos: Static onboarding: The object is static and the camera is moving around capturing all possible object views. Two videos are available (one upright and one upside-down). Object masks and 6D object poses are available for all video frames.

This year, we aim to bridge this gap by introducing new model-free tasks where CAD models of test objects are not available and methods need to rapidly learn new objects just from reference videos in max 5 min on 1 GPU.

While the model-based tasks are relevant for warehouse or factory settings, where CAD models of the target objects are often available, its applicability is limited in open-world scenarios.