57 citations · 61 across the 14 of their papers we have counts for
15 papers
GraspGraphNet: Graph-Structured Multi-Embodiment Dexterous Grasp Generation
Yeonseo Lee, Taeyeop Lee, Hyosup Shin +2
Dexterous grasp generation across robot hands is challenging because hands differ in kinematic topology, actuation dimensions, and native command spaces. We introduce GraspGraphNet…
Event6D: Event-based Novel Object 6D Pose Tracking
Jae-Young Kang, Hoonhee Cho, Taeyeop Lee +4
Event cameras provide microsecond latency, making them suitable for 6D object pose tracking in fast, dynamic scenes where conventional RGB and depth pipelines suffer from motion bl…
GeoNVS: Geometry Grounded Video Diffusion for Novel View Synthesis
Minjun Kang, Inkyu Shin, Taeyeop Lee +3
Novel view synthesis requires strong 3D geometric consistency and the ability to generate visually coherent images across diverse viewpoints. While recent camera-controlled video d…
XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generation
Yeonseo Lee, Jungwook Mun, Hyosup Shin +4
Real-world robotic systems frequently require diverse end-effectors for different tasks, however most existing grasp detection methods are optimized for a single gripper type, dema…
DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation
Taeyeop Lee, Gyuree Kang, Bowen Wen +5
Despite the prevalence of transparent object interactions in human everyday life, transparent robotic manipulation research remains limited to short-horizon tasks and basic graspin…
Drag4D: Align Your Motion with Text-Driven 3D Scene Generation
Minjun Kang, Inkyu Shin, Taeyeop Lee +2
We introduce Drag4D, an interactive framework that integrates object motion control within text-driven 3D scene generation. This framework enables users to define 3D trajectories f…
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…
GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes
Seunghyeok Back, Joosoon Lee, Kangmin Kim +8
Robust grasping in cluttered environments remains an open challenge in robotics. While benchmark datasets have significantly advanced deep learning methods, they mainly focus on si…
Any6D: Model-free 6D Pose Estimation of Novel Objects
Taeyeop Lee, Bowen Wen, Minjun Kang +3
We introduce Any6D, a model-free framework for 6D object pose estimation that requires only a single RGB-D anchor image to estimate both the 6D pose and size of unknown objects in…
Stable Surface Regularization for Fast Few-Shot NeRF
Byeongin Joung, Byeong-Uk Lee, Jaesung Choe +5
This paper proposes an algorithm for synthesizing novel views under few-shot setup. The main concept is to develop a stable surface regularization technique called Annealing Signed…
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
Taeyeop Lee, Jonathan Tremblay, Valts Blukis +6
Test-time adaptation methods have been gaining attention recently as a practical solution for addressing source-to-target domain gaps by gradually updating the model without requir…
One-Shot Neural Fields for 3D Object Understanding
Valts Blukis, Taeyeop Lee, Jonathan Tremblay +5
We present a unified and compact scene representation for robotics, where each object in the scene is depicted by a latent code capturing geometry and appearance. This representati…
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation
Taeyeop Lee, Byeong-Uk Lee, Inkyu Shin +4
Learning to estimate object pose often requires ground-truth (GT) labels, such as CAD model and absolute-scale object pose, which is expensive and laborious to obtain in the real w…
Category-Level Metric Scale Object Shape and Pose Estimation
Taeyeop Lee, Byeong-Uk Lee, Myungchul Kim +1
Advances in deep learning recognition have led to accurate object detection with 2D images. However, these 2D perception methods are insufficient for complete 3D world information.…
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
Seokju Lee, Junsik Kim, Jae Shin Yoon +7
In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point…