82 citations · 85 across the 17 of their papers we have counts for
18 papers
Embodied Snap: Octopus-Inspired Distributed Reach-and-Attach with a Speed-Limited Soft Arm
Linxin Hou, Zhihang Qin, Heyang Zou +5
Reach-and-attach of soft robotic arms with passive suction requires accurate targeting and sufficient contact speed, yet geared actuators can impose a speed limit that improved tra…
Artiverse: A Diverse and Physically Grounded Dataset for Articulated Objects
Denys Iliash, Jiayi Liu, Egor Fokin +4
We present Artiverse, a diverse and physically grounded dataset of high-quality articulated 3D objects designed for realistic functional modeling and simulation. Artiverse contains…
JRM: Joint Reconstruction Model for Multiple Objects without Alignment
Qirui Wu, Yawar Siddiqui, Duncan Frost +6
Object-centric reconstruction seeks to recover the 3D structure of a scene through composition of independent objects. While this independence can simplify modeling, it discards st…
Better Matching, Less Forgetting: A Quality-Guided Matcher for Transformer-based Incremental Object Detection
Qirui Wu, Shizhou Zhang, De Cheng +4
Incremental Object Detection (IOD) aims to continuously learn new object classes without forgetting previously learned ones. A persistent challenge is catastrophic forgetting, prim…
Octopus-inspired Distributed Control for Soft Robotic Arms: A Graph Neural Network-Based Attention Policy with Environmental Interaction
Linxin Hou, Qirui Wu, Zhihang Qin +2
This paper proposes SoftGM, an octopus-inspired distributed control architecture for segmented soft robotic arms that learn to reach targets in contact-rich environments using onli…
ShapeR: Robust Conditional 3D Shape Generation from Casual Captures
Yawar Siddiqui, Duncan Frost, Samir Aroudj +9
Recent advances in 3D shape generation have achieved impressive results, but most existing methods rely on clean, unoccluded, and well-segmented inputs. Such conditions are rarely…
YOLO-IOD: Towards Real Time Incremental Object Detection
Shizhou Zhang, Xueqiang Lv, Yinghui Xing +4
Current methods for incremental object detection (IOD) primarily rely on Faster R-CNN or DETR series detectors; however, these approaches do not accommodate the real-time YOLO dete…
A Quantitative Comparison of Centralised and Distributed Reinforcement Learning-Based Control for Soft Robotic Arms
Linxin Hou, Qirui Wu, Zhihang Qin +3
This paper presents a quantitative comparison between centralised and distributed multi-agent reinforcement learning (MARL) architectures for controlling a soft robotic arm modelle…
Digital Twin Generation from Visual Data: A Survey
Andrew Melnik, Benjamin Alt, Giang Nguyen +7
This survey examines recent advances in generating digital twins from visual data. These digital twins - virtual 3D replicas of physical assets - can be applied to robotics, media…
Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector
Qirui Wu, Shizhou Zhang, De Cheng +4
Catastrophic forgetting is a critical chanllenge for incremental object detection (IOD). Most existing methods treat the detector monolithically, relying on instance replay or know…
Diorama: Unleashing Zero-shot Single-view 3D Indoor Scene Modeling
Qirui Wu, Denys Iliash, Daniel Ritchie +2
Reconstructing structured 3D scenes from RGB images using CAD objects unlocks efficient and compact scene representations that maintain compositionality and interactability. Existi…
R3DS: Reality-linked 3D Scenes for Panoramic Scene Understanding
Qirui Wu, Sonia Raychaudhuri, Daniel Ritchie +2
We introduce the Reality-linked 3D Scenes (R3DS) dataset of synthetic 3D scenes mirroring the real-world scene arrangements from Matterport3D panoramas. Compared to prior work, R3D…
Generalizing Single-View 3D Shape Retrieval to Occlusions and Unseen Objects
Qirui Wu, Daniel Ritchie, Manolis Savva +1
Single-view 3D shape retrieval is a challenging task that is increasingly important with the growth of available 3D data. Prior work that has studied this task has not focused on e…
Physics-enhanced Gaussian Process Variational Autoencoder
Thomas Beckers, Qirui Wu, George J. Pappas
Variational autoencoders allow to learn a lower-dimensional latent space based on high-dimensional input/output data. Using video clips as input data, the encoder may be used to de…
Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model
Yinghui Xing, Qirui Wu, De Cheng +4
With the emergence of large pre-trained vison-language model like CLIP, transferable representations can be adapted to a wide range of downstream tasks via prompt tuning. Prompt tu…
D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding
Dave Zhenyu Chen, Qirui Wu, Matthias Nießner +1
Recent studies on dense captioning and visual grounding in 3D have achieved impressive results. Despite developments in both areas, the limited amount of available 3D vision-langua…
Resilient Control of Platooning Networked Robotic Systems via Dynamic Watermarking
Matthew Porter, Arnav Joshi, Sidhartha Dey +5
Networked robotic systems, such as connected vehicle platoons, can improve the safety and efficiency of transportation networks by allowing for high-speed coordination. To enable s…
Plan2Scene: Converting Floorplans to 3D Scenes
Madhawa Vidanapathirana, Qirui Wu, Yasutaka Furukawa +2
We address the task of converting a floorplan and a set of associated photos of a residence into a textured 3D mesh model, a task which we call Plan2Scene. Our system 1) lifts a fl…