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• W2952807897 abstract "Indoor scenes exhibit rich hierarchical structure in 3D object layouts. Many tasks in 3D scene understanding can benefit from reasoning jointly about the hierarchical context of a scene, and the identities of objects. We present a variational denoising recursive autoencoder (VDRAE) that generates and iteratively refines a hierarchical representation of 3D object layouts, interleaving bottom-up encoding for context aggregation and top-down decoding for propagation. We train our VDRAE on large-scale 3D scene datasets to predict both instance-level segmentations and a 3D object detections from an over-segmentation of an input point cloud. We show that our VDRAE improves object detection performance on real-world 3D point cloud datasets compared to baselines from prior work." @default.
• W2952807897 created "2019-06-27" @default.
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• W2952807897 date "2019-03-09" @default.
• W2952807897 modified "2023-09-23" @default.
• W2952807897 title "Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction" @default.
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• W2952807897 doi "https://doi.org/10.48550/arxiv.1903.03757" @default.
• W2952807897 hasPublicationYear "2019" @default.
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