6 papers
How Noisy Poses Break Inverse Dynamics: Analysis and Mitigation for Video-Based Joint Torque Estimation
Donghyun Kim, Chanyoung Kim, Eunseo Jeong +2
Recent advances in monocular 3D human pose estimation enable accurate body tracking from video. However, translating these kinematic estimates into physical quantities, such as joi…
Rethinking Graph Convolution for 2D-to-3D Hand Pose Lifting
Chanyoung Kim, Donghyun Kim, Dong-Hyun Sim +2
Graph convolutional networks (GCNs) are widely used for 3D hand pose estimation, where the hand skeleton is encoded as a fixed adjacency graph. We revisit whether this is the most…
Think as Needed: Geometry-Driven Adaptive Perception for Autonomous Driving
Donghyun Kim, Jaehyoung Park
Autonomous driving scenes range from empty highways to dense intersections with dozens of interacting road users, yet current 3D detection models apply a fixed computation budget t…
Delaunay Canopy: Building Wireframe Reconstruction from Airborne LiDAR Point Clouds via Delaunay Graph
Donghyun Kim, Chanyoung Kim, Youngjoong Kwon +1
Reconstructing building wireframe from airborne LiDAR point clouds yields a compact, topology-centric representation that enables structural understanding beyond dense meshes. Yet…
Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes
Donghyun Kim, Chanyoung Kim, Hyunah Ko +1
While 3D point clouds are widely used in vision applications, their irregular and sparse nature make them challenging to handle. In response, numerous encoding approaches have been…
PLATYPUS: Progressive Local Surface Estimator for Arbitrary-Scale Point Cloud Upsampling
Donghyun Kim, Hyeonkyeong Kwon, Yumin Kim +1
3D point clouds are increasingly vital for applications like autonomous driving and robotics, yet the raw data captured by sensors often suffer from noise and sparsity, creating ch…