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Research map / Point clouds / Segmentation and detection

Unsupervised Feature Learning for 3D Scene Reconstruction with Occupancy Maps

AAAI 2017 · Point clouds › Segmentation and detection · approach: Scene completion and instance segmentation · 7 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
orientation clustering reduces cluster count from initial clusters z to planar clusters wPS-HM · Roomnumber of clusters: 8120 -> 42Helped
orientation clustering reduces cluster count from initial clusters z to planar clusters wPS-HM · Corridornumber of clusters: 7637 -> 83Helped
orientation clustering reduces cluster count from initial clusters z to planar clusters wPS-HM · Outdoornumber of clusters: 10560 -> 23Mixed
clustering reduces points to initial clustersASK-means · Roomnumber of elements: 582463 -> 8120Helped
PS-HM versus LARD-HM and Octo-Map in structured environmentsHilbert Maps · Room and Corridorclassification accuracyHelped
PS-HM in predominantly non-planar outdoor dataPS-HM · Outdoorclassification accuracyNo clear effect
add anisotropic nearest-neighbor search with length-scale weighting instead of Euclidean kd-tree (ablation)PS-HM · sparse clustersneighbor association qualityHelped
add 3D orientation alignment step after orientation clustering (ablation)orientation clustering · 3D extruded datasetwall rotation correctnessHelped
add unoccupied points sampled between sensor and occupied points (ablation)orientation clustering · Roomreconstruction detailHelped
orientation clustering step added to spatial clusteringpreprocessing · all datasetsprocessing time: 20%Hurt

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