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- W2422498169 abstract "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." @default.
- W2422498169 created "2016-06-24" @default.
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- W2422498169 date "2016-06-13" @default.
- W2422498169 modified "2023-09-27" @default.
- W2422498169 title "Visual-Inertial Scene Representations." @default.
- W2422498169 hasPublicationYear "2016" @default.
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