68 citations · 166 across the 21 of their papers we have counts for
25 papers
SHOW3D: Capturing Scenes of 3D Hands and Objects in the Wild
Patrick Rim, Kevin Harris, Braden Copple +8
Accurate 3D understanding of human hands and objects during manipulation remains a significant challenge for egocentric computer vision. Existing hand-object interaction datasets a…
EgoPoseFormer v2: Accurate Egocentric Human Motion Estimation for AR/VR
Zhenyu Li, Sai Kumar Dwivedi, Filip Maric +11
Egocentric human motion estimation is essential for AR/VR experiences, yet remains challenging due to limited body coverage from the egocentric viewpoint, frequent occlusions, and…
Ego-Exo 3D Hand Tracking in the Wild with a Mobile Multi-Camera Rig
Patrick Rim, Kun He, Kevin Harris +7
Accurate 3D tracking of hands and their interactions with the world in unconstrained settings remains a significant challenge for egocentric computer vision. With few exceptions, e…
GoTrack: Generic 6DoF Object Pose Refinement and Tracking
Van Nguyen Nguyen, Christian Forster, Sindi Shkodrani +4
We introduce GoTrack, an efficient and accurate CAD-based method for 6DoF object pose refinement and tracking, which can handle diverse objects without any object-specific training…
BOP Challenge 2024 on Model-Based and Model-Free 6D Object Pose Estimation
Van Nguyen Nguyen, Stephen Tyree, Andrew Guo +16
We present the evaluation methodology, datasets and results of the BOP Challenge 2024, the 6th in a series of public competitions organized to capture the state of the art in 6D ob…
HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos
Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon +11
We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (3.7M+ images) of recordings that feature 19 sub…
Introducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking
Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon +9
We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (more than 3.7M images) of multi-view RGB/monoch…
BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects
Tomas Hodan, Martin Sundermeyer, Yann Labbe +7
We present the evaluation methodology, datasets and results of the BOP Challenge 2023, the fifth in a series of public competitions organized to capture the state of the art in mod…
DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions
Sammy Christen, Shreyas Hampali, Fadime Sener +5
Generating natural hand-object interactions in 3D is challenging as the resulting hand and object motions are expected to be physically plausible and semantically meaningful. Furth…
FoundPose: Unseen Object Pose Estimation with Foundation Features
Evin Pınar Örnek, Yann Labbé, Bugra Tekin +4
We propose FoundPose, a model-based method for 6D pose estimation of unseen objects from a single RGB image. The method can quickly onboard new objects using their 3D models withou…
CNOS: A Strong Baseline for CAD-based Novel Object Segmentation
Van Nguyen Nguyen, Thibault Groueix, Georgy Ponimatkin +2
We propose a simple three-stage approach to segment unseen objects in RGB images using their CAD models. Leveraging recent powerful foundation models, DINOv2 and Segment Anything,…
AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation
Takehiko Ohkawa, Kun He, Fadime Sener +3
We present AssemblyHands, a large-scale benchmark dataset with accurate 3D hand pose annotations, to facilitate the study of egocentric activities with challenging hand-object inte…
BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects
Martin Sundermeyer, Tomas Hodan, Yann Labbe +5
We present the evaluation methodology, datasets and results of the BOP Challenge 2022, the fourth in a series of public competitions organized with the goal to capture the status q…
In-Hand 3D Object Scanning from an RGB Sequence
Shreyas Hampali, Tomas Hodan, Luan Tran +3
We propose a method for in-hand 3D scanning of an unknown object with a monocular camera. Our method relies on a neural implicit surface representation that captures both the geome…
UmeTrack: Unified multi-view end-to-end hand tracking for VR
Shangchen Han, Po-chen Wu, Yubo Zhang +13
Real-time tracking of 3D hand pose in world space is a challenging problem and plays an important role in VR interaction. Existing work in this space are limited to either producin…
Neural Correspondence Field for Object Pose Estimation
Lin Huang, Tomas Hodan, Lingni Ma +7
We propose a method for estimating the 6DoF pose of a rigid object with an available 3D model from a single RGB image. Unlike classical correspondence-based methods which predict 3…
LISA: Learning Implicit Shape and Appearance of Hands
Enric Corona, Tomas Hodan, Minh Vo +4
This paper proposes a do-it-all neural model of human hands, named LISA. The model can capture accurate hand shape and appearance, generalize to arbitrary hand subjects, provide de…
Pose Estimation of Specific Rigid Objects
Tomas Hodan
In this thesis, we address the problem of estimating the 6D pose of rigid objects from a single RGB or RGB-D input image, assuming that 3D models of the objects are available. This…
BOP Challenge 2020 on 6D Object Localization
Tomas Hodan, Martin Sundermeyer, Bertram Drost +5
This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the…
Learning Surrogates via Deep Embedding
Yash Patel, Tomas Hodan, Jiri Matas
This paper proposes a technique for training a neural network by minimizing a surrogate loss that approximates the target evaluation metric, which may be non-differentiable. The su…
EPOS: Estimating 6D Pose of Objects with Symmetries
Tomas Hodan, Daniel Barath, Jiri Matas
We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image. The method is applicable to a broad range of objects, in…
Photorealistic Image Synthesis for Object Instance Detection
Tomas Hodan, Vibhav Vineet, Ran Gal +6
We present an approach to synthesize highly photorealistic images of 3D object models, which we use to train a convolutional neural network for detecting the objects in real images…
A Summary of the 4th International Workshop on Recovering 6D Object Pose
Tomas Hodan, Rigas Kouskouridas, Tae-Kyun Kim +12
This document summarizes the 4th International Workshop on Recovering 6D Object Pose which was organized in conjunction with ECCV 2018 in Munich. The workshop featured four invited…
BOP: Benchmark for 6D Object Pose Estimation
Tomas Hodan, Frank Michel, Eric Brachmann +13
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the obj…