68 citations · 96 across the 15 of their papers we have counts for
15 papers
BenchSeg: A Large-Scale Dataset and Benchmark for Multi-View Food Video Segmentation
Ahmad AlMughrabi, Guillermo Rivo, Carlos Jiménez-Farfán +6
Food image segmentation is a critical task for dietary analysis, enabling accurate estimation of food volume and nutrients. However, current methods suffer from limited multi-view…
GCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation
Weihang Li, Hongli Xu, Junwen Huang +4
A key challenge in model-free category-level pose estimation is the extraction of contextual object features that generalize across varying instances within a specific category. Re…
SCRREAM : SCan, Register, REnder And Map:A Framework for Annotating Accurate and Dense 3D Indoor Scenes with a Benchmark
HyunJun Jung, Weihang Li, Shun-Cheng Wu +8
Traditionally, 3d indoor datasets have generally prioritized scale over ground-truth accuracy in order to obtain improved generalization. However, using these datasets to evaluate…
Deformable 3D Gaussian Splatting for Animatable Human Avatars
HyunJun Jung, Nikolas Brasch, Jifei Song +5
Recent advances in neural radiance fields enable novel view synthesis of photo-realistic images in dynamic settings, which can be applied to scenarios with human animation. Commonl…
Polarimetric Information for Multi-Modal 6D Pose Estimation of Photometrically Challenging Objects with Limited Data
Patrick Ruhkamp, Daoyi Gao, HyunJun Jung +2
6D pose estimation pipelines that rely on RGB-only or RGB-D data show limitations for photometrically challenging objects with e.g. textureless surfaces, reflections or transparenc…
Multi-Modal Dataset Acquisition for Photometrically Challenging Object
HyunJun Jung, Patrick Ruhkamp, Nassir Navab +1
This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging ob…
Robust Monocular Depth Estimation under Challenging Conditions
Stefano Gasperini, Nils Morbitzer, HyunJun Jung +2
While state-of-the-art monocular depth estimation approaches achieve impressive results in ideal settings, they are highly unreliable under challenging illumination and weather con…
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks
HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai +10
Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks…
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
HyunJun Jung, Guangyao Zhai, Shun-Cheng Wu +10
Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches, research is shifting towards category-level pose estimation…
MonoGraspNet: 6-DoF Grasping with a Single RGB Image
Guangyao Zhai, Dianye Huang, Shun-Cheng Wu +6
6-DoF robotic grasping is a long-lasting but unsolved problem. Recent methods utilize strong 3D networks to extract geometric grasping representations from depth sensors, demonstra…
PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects
Pengyuan Wang, HyunJun Jung, Yitong Li +6
Object pose estimation is crucial for robotic applications and augmented reality. Beyond instance level 6D object pose estimation methods, estimating category-level pose and shape…
Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression
HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai +9
Depth estimation is a core task in 3D computer vision. Recent methods investigate the task of monocular depth trained with various depth sensor modalities. Every sensor has its adv…
Polarimetric Pose Prediction
Daoyi Gao, Yitong Li, Patrick Ruhkamp +6
Light has many properties that vision sensors can passively measure. Colour-band separated wavelength and intensity are arguably the most commonly used for monocular 6D object pose…
Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments
HyunJun Jung, Nikolas Brasch, Ales Leonardis +2
Indirect Time-of-Flight (I-ToF) imaging is a widespread way of depth estimation for mobile devices due to its small size and affordable price. Previous works have mainly focused on…
I Like to Move It: 6D Pose Estimation as an Action Decision Process
Benjamin Busam, Hyun Jun Jung, Nassir Navab
Object pose estimation is an integral part of robot vision and AR. Previous 6D pose retrieval pipelines treat the problem either as a regression task or discretize the pose space t…