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• W3147808988 abstract "Constructing and maintaining a consistent scene model on-the-fly is the core task for online spatial perception, interpretation, and action. In this paper, we represent the scene with a Bayesian nonparametric mixture model, seamlessly describing per-point occupancy status with a continuous probability density function. Instead of following the conventional data fusion paradigm, we address the problem of online learning the process how sequential point cloud data are generated from the scene geometry. An incremental and parallel inference is performed to update the parameter space in real-time. We experimentally show that the proposed representation achieves state-of-the-art accuracy with promising efficiency. The consistent probabilistic formulation assures a generative model that is adaptive to different sensor characteristics, and the model complexity can be dynamically adjusted on-the-fly according to different data scales." @default.
• W3147808988 created "2021-04-13" @default.
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• W3147808988 date "2021-03-31" @default.
• W3147808988 modified "2023-10-18" @default.
• W3147808988 title "Online Learning of a Probabilistic and Adaptive Scene Representation" @default.
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• W3147808988 doi "https://doi.org/10.48550/arxiv.2103.16832" @default.
• W3147808988 hasPublicationYear "2021" @default.
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