Visual-Inertial Semantic Repres4ntations

Stefano Soatto

UCLA Henry Samueli School of Engineering and Applied Science

RP-ID  :  160005

学科分类：计算机科学（综合）

美国|英语

来源: UCLA Computer Science Technical Reports Database

【 摘 要 】

We describe a representation of a scene that captures geometric and semantic attributes of objects within, along with their uncertainty. Objects are assumed persistent in the scene, and their likelihood computed from intermittent visual data using a convolutional architecture, integrated within a Bayesian filtering framework with inertials and a context model. Our method yields a posterior estimate of geometry (attributed point cloud and associated uncertainty), semantics (identities and co-occurrence), and a point-estimate of topology for a variable number of objects within the scene, implemented causally and in real-time on commodity hardware.