Acquiring 3D indoor environments with variability and repetition

By Y. Kim, Niloy J. Mitra, Dong‐Ming Yan, Leonidas J. Guibas. 2012

199 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, 3D Vision & Pose

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Multiview Compressive Coding for 3D Reconstruction (2023, methodology): “For scenes, techniques include plane fitting [38], 3D model fitting and retrieval [14, 39] or leverage symmetries [27] and predict 3D semantics [3, 13, 58].”
Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis (2016, methodology): “Another idea is to ﬁnd identical CAD models in a shape database for a given partial input shape and align it with the scan [24, 33, 16, 21, 35].”
Semantic Scene Completion from a Single Depth Image (2016, background): “One possible approach to obtain the complete geometry and semantic labels for a scene is to retrieve and fit instance-level 3D mesh models to the observed depth map [7, 30, 4, 16, 23, 17, 14].”
3D Object Reconstruction from a Single Depth View with Adversarial Learning (2017, methodology): “Given a partial shape input, [25] [29] [36] [45] [47] try to retrieve an identical or most likely CAD model and align it with the partial scan.”
Sliding Shapes for 3D Object Detection in Depth Images (2014, background): “Since our generic object detector does not reply on any assumption about the background or requires a dominant supporting plane [15, 16], and its single-view nature doesn’t require a (semi-)complete scan of an object [17], it can be used as a basic building block for general scene understanding task”
SemanticPaint (2015, methodology): “Our entire semantic processing pipeline runs at interactive rates on a commodity desktop PC with a single GPU”
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans (2017, unknown): “One can also ‘complete’ shapes by replacing scanned geometry with aligned CAD models retrieved from a database [20, 32, 15, 17, 33].”
Learning part-based templates from large collections of 3D shapes (2013, background): “…classes and thus should be a valuable resource for data-driven solutions to common geometry processing problems, including surface reconstruction [Kim et al. 2012b], model completion [Shen et al. 2012], model-based object recognition [Shapira et al. 2010; Nan et al. 2012], shape synthesis…”
A Survey of Surface Reconstruction from Point Clouds (2017, methodology): “[KBH06] general implicitﬁeld GraphCut[HK06] general volumetricsegmentation UnorientedIndicator[ACSTD07] general implicitﬁeld LOP[LCOLTE07] general pointset DictionaryLearning[XZZ ∗ 14] general mesh Visibility VRIP[CL96] general implicitﬁeld TVL1-VRIP[ZPB07] general implicitﬁeld SigningtheUnsigned[MD”
A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation (2016, unknown): “, [29], [30], [31], [32], or part-based models, e.”
Scan2CAD: Learning CAD Model Alignment in RGB-D Scans (2018, methodology): “[24] learn a shape prior in the form of a deformable part model from input scans to find matches at test time; or AA2h [23] use a similar approach to PPF, where a histogram of normal distributions of sample points is used for retrieval.”
Data-driven structural priors for shape completion (2015, background): “…and parts reduce complexity of the representation [Liu et al. 2014]), and generalizing the symmetry term to a regularization term that favors repetitions on different levels of the hierarchy (i.e., objects often repeat in a scene, but transformation typically cannot be learned [Kim et al. 2012]).”
3D Object Dense Reconstruction from a Single Depth View (2018, unknown): “Given a partial shape input, [31] [32] [33] [34] [35] [36] try to retrieve an identical or most likely 3D model and align it with the partial scan.”
A search-classify approach for cluttered indoor scene understanding (2012, background): “Keywords: point cloud classification, scene understanding, reconstruction Links: DL PDF”
RAPter (2015, methodology): “2012], office environments [Kim et al. 2012; Mattausch et al. 2014], and also for outdoor buildings [Zheng et al.”