On Being the Right Scale: Sizing Large Collections of 3D Models
Stanford University
SIGGRAPH Asia 2014 Workshop on Indoor Scene Understanding: Where Graphics Meets Vision
Abstract
We address the problem of recovering reliable sizes for a collection of models defined using scales with unknown correspondence to physical units. Our algorithmic approach provides absolute size estimates for 3D models by combining category-based size priors and size observations from 3D scenes. Our approach handles unobserved 3D models without any user intervention. It also scales to large public 3D model databases and is appropriate for handling the open-world problem of rapidly expanding collections of 3D models. We use two datasets from online 3D model repositories to evaluate against both human judgments of size and ground truth physical sizes of 3D models, and find that an algorithmic approach can predict sizes more accurately than people.