GenRecon lifts object-level generative priors to scene-scale reconstruction by chunking scenes and using projection-based conditioning on multi-view features, claiming 16% better results than prior methods.
Taylor, Mathias Unberath, Ming-Yu Liu, and Chen-Hsuan Lin
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
TwinOR creates dynamic photorealistic digital twins of operating rooms that generate realistic RGB and depth data enabling embodied AI perception and localization tasks to match real-world performance levels.
citing papers explorer
-
GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction
GenRecon lifts object-level generative priors to scene-scale reconstruction by chunking scenes and using projection-based conditioning on multi-view features, claiming 16% better results than prior methods.
-
TwinOR: Photorealistic Digital Twins of Dynamic Operating Rooms for Embodied AI Research
TwinOR creates dynamic photorealistic digital twins of operating rooms that generate realistic RGB and depth data enabling embodied AI perception and localization tasks to match real-world performance levels.