33 citations · 51 across the 12 of their papers we have counts for
12 papers
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…
VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis
Sen Wang, Qing Cheng, Stefano Gasperini +5
The generation of high-fidelity view synthesis is essential for robotic navigation and interaction but remains challenging, particularly in indoor environments and real-time scenar…
Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model
Yin Wang, Zhiying Leng, Frederick W. B. Li +2
Text-driven human motion generation in computer vision is both significant and challenging. However, current methods are limited to producing either deterministic or imprecise moti…
Dynamic Hyperbolic Attention Network for Fine Hand-object Reconstruction
Zhiying Leng, Shun-Cheng Wu, Mahdi Saleh +6
Reconstructing both objects and hands in 3D from a single RGB image is complex. Existing methods rely on manually defined hand-object constraints in Euclidean space, leading to sub…
Incremental 3D Semantic Scene Graph Prediction from RGB Sequences
Shun-Cheng Wu, Keisuke Tateno, Nassir Navab +1
3D semantic scene graphs are a powerful holistic representation as they describe the individual objects and depict the relation between them. They are compact high-level graphs tha…
CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion
Guangyao Zhai, Evin Pınar Örnek, Shun-Cheng Wu +4
Controllable scene synthesis aims to create interactive environments for various industrial use cases. Scene graphs provide a highly suitable interface to facilitate these applicat…
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…
Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport
Mahdi Saleh, Shun-Cheng Wu, Luca Cosmo +3
Shape matching has been a long-studied problem for the computer graphics and vision community. The objective is to predict a dense correspondence between meshes that have a certain…
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
Shun-Cheng Wu, Johanna Wald, Keisuke Tateno +2
Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to incrementally build up semanti…
SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion
Shun-Cheng Wu, Keisuke Tateno, Nassir Navab +1
Real-time scene reconstruction from depth data inevitably suffers from occlusion, thus leading to incomplete 3D models. Partial reconstructions, in turn, limit the performance of a…
Unsupervised Monocular Depth Prediction for Indoor Continuous Video Streams
Yinglong Feng, Shuncheng Wu, Okan Köpüklü +2
This paper studies unsupervised monocular depth prediction problem. Most of existing unsupervised depth prediction algorithms are developed for outdoor scenarios, while the depth p…