Proceedings ArticleDOI
3D Semantic Parsing of Large-Scale Indoor Spaces
Iro Armeni,Ozan Sener,Amir Roshan Zamir,Helen Jiang,Ioannis Brilakis,Martin Fischer,Silvio Savarese +6 more
- pp 1534-1543
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TLDR
This paper argues that identification of structural elements in indoor spaces is essentially a detection problem, rather than segmentation which is commonly used, and proposes a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach.Abstract:
In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e.g. rooms, etc) that are aligned into a canonical reference coordinate system. Second, the spaces are parsed into their structural and building elements (e.g. walls, columns, etc). Performing these with a strong notation of global 3D space is the backbone of our method. The alignment in the first step injects strong 3D priors from the canonical coordinate system into the second step for discovering elements. This allows diverse challenging scenarios as man-made indoor spaces often show recurrent geometric patterns while the appearance features can change drastically. We also argue that identification of structural elements in indoor spaces is essentially a detection problem, rather than segmentation which is commonly used. We evaluated our method on a new dataset of several buildings with a covered area of over 6, 000m2 and over 215 million points, demonstrating robust results readily useful for practical applications.read more
Citations
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Proceedings ArticleDOI
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
TL;DR: This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.
Journal ArticleDOI
Dynamic Graph CNN for Learning on Point Clouds
TL;DR: This work proposes a new neural network module suitable for CNN-based high-level tasks on point clouds, including classification and segmentation called EdgeConv, which acts on graphs dynamically computed in each layer of the network.
Proceedings ArticleDOI
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
TL;DR: This work introduces ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations, and shows that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks.
Proceedings ArticleDOI
KPConv: Flexible and Deformable Convolution for Point Clouds
Hugues Thomas,Charles R. Qi,Jean-Emmanuel Deschaud,Beatriz Marcotegui,François Goulette,Leonidas J. Guibas +5 more
TL;DR: KPConv is a new design of point convolution, i.e. that operates on point clouds without any intermediate representation, that outperform state-of-the-art classification and segmentation approaches on several datasets.
Proceedings Article
PointCNN: convolution on Χ -transformed points
TL;DR: This work proposes to learn an Χ-transformation from the input points to simultaneously promote two causes: the first is the weighting of the input features associated with the points, and the second is the permutation of the points into a latent and potentially canonical order.
References
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