82 citations · 85 across the 17 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
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…