22 citations · 25 across the 6 of their papers we have counts for
11 papers
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…
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…
VertexRegen: Mesh Generation with Continuous Level of Detail
Xiang Zhang, Yawar Siddiqui, Armen Avetisyan +3
We introduce VertexRegen, a novel mesh generation framework that enables generation at a continuous level of detail. Existing autoregressive methods generate meshes in a partial-to…
Human-in-the-Loop Local Corrections of 3D Scene Layouts via Infilling
Christopher Xie, Armen Avetisyan, Henry Howard-Jenkins +5
We present a novel human-in-the-loop approach to estimate 3D scene layout that uses human feedback from an egocentric standpoint. We study this approach through introduction of a n…
SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Model
Armen Avetisyan, Christopher Xie, Henry Howard-Jenkins +11
We introduce SceneScript, a method that directly produces full scene models as a sequence of structured language commands using an autoregressive, token-based approach. Our propose…
ReplaceAnything3D:Text-Guided 3D Scene Editing with Compositional Neural Radiance Fields
Edward Bartrum, Thu Nguyen-Phuoc, Chris Xie +5
We introduce ReplaceAnything3D model (RAM3D), a novel text-guided 3D scene editing method that enables the replacement of specific objects within a scene. Given multi-view images o…
OrienterNet: Visual Localization in 2D Public Maps with Neural Matching
Paul-Edouard Sarlin, Daniel DeTone, Tsun-Yi Yang +7
Humans can orient themselves in their 3D environments using simple 2D maps. Differently, algorithms for visual localization mostly rely on complex 3D point clouds that are expensiv…
SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans
Armen Avetisyan, Tatiana Khanova, Christopher Choy +3
We present a novel approach to reconstructing lightweight, CAD-based representations of scanned 3D environments from commodity RGB-D sensors. Our key idea is to jointly optimize fo…
RIO: 3D Object Instance Re-Localization in Changing Indoor Environments
Johanna Wald, Armen Avetisyan, Nassir Navab +2
In this work, we introduce the task of 3D object instance re-localization (RIO): given one or multiple objects in an RGB-D scan, we want to estimate their corresponding 6DoF poses…
End-to-End CAD Model Retrieval and 9DoF Alignment in 3D Scans
Armen Avetisyan, Angela Dai, Matthias Nießner
We present a novel, end-to-end approach to align CAD models to an 3D scan of a scene, enabling transformation of a noisy, incomplete 3D scan to a compact, CAD reconstruction with c…
Scan2CAD: Learning CAD Model Alignment in RGB-D Scans
Armen Avetisyan, Manuel Dahnert, Angela Dai +3
We present Scan2CAD, a novel data-driven method that learns to align clean 3D CAD models from a shape database to the noisy and incomplete geometry of a commodity RGB-D scan. For a…