Proceedings ArticleDOI
Geometrically consistent plane extraction for dense indoor 3D maps segmentation
Trung Pham,Markus Eich,Ian Reid,Gordon Wyeth +3 more
- pp 4199-4204
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TLDR
This paper presents an unsupervised geometric-based approach for the segmentation of 3D point clouds into objects and meaningful scene structures and proposes a novel global plane extraction algorithm for robustly discovering the underlying planes in the scene.Abstract:
Modern SLAM systems with a depth sensor are able to reliably reconstruct dense 3D geometric maps of indoor scenes. Representing these maps in terms of meaningful entities is a step towards building semantic maps for autonomous robots. One approach is to segment the 3D maps into semantic objects using Conditional Random Fields (CRF), which requires large 3D ground truth datasets to train the classification model. Additionally, the CRF inference is often computationally expensive. In this paper, we present an unsupervised geometric-based approach for the segmentation of 3D point clouds into objects and meaningful scene structures. We approximate an input point cloud by an adjacency graph over surface patches, whose edges are then classified as being either on or off. We devise an effective classifier which utilises both global planar surfaces and local surface convexities for edge classification. More importantly, we propose a novel global plane extraction algorithm for robustly discovering the underlying planes in the scene. Our algorithm is able to enforce the extracted planes to be mutually orthogonal or parallel which conforms usually with human-made indoor environments. We reconstruct 654 3D indoor scenes from NYUv2 sequences to validate the efficiency and effectiveness of our segmentation method.read more
Citations
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Proceedings ArticleDOI
Meaningful maps with object-oriented semantic mapping
TL;DR: In this article, the authors address the problem of building environmental maps that include both semantically meaningful, object-level entities and point- or mesh-based geometrical representations.
Journal ArticleDOI
An efficient global energy optimization approach for robust 3D plane segmentation of point clouds
TL;DR: This paper formulates the plane segmentation problem as a global energy optimization because it is robust to high levels of noise and clutter and obtained good performances both in high-quality TLS point clouds and low-quality RGB-D point clouds.
Proceedings ArticleDOI
The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research
Jürgen Leitner,Adam W. Tow,Niko Sünderhauf,Jake E. Dean,Joseph W. Durham,Matthew Cooper,Markus Eich,Christopher Lehnert,Ruben Mangels,Chris McCool,Peter T. Kujala,Lachlan Nicholson,Trung Pham,James Sergeant,Liao Wu,Fangyi Zhang,Ben Upcroft,Peter Corke +17 more
TL;DR: The ACRV Picking Benchmark as discussed by the authors is a new physical benchmark for robotic picking, which is designed to be reproducible and consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils.
Proceedings ArticleDOI
SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes
TL;DR: SceneCut as mentioned in this paper uses a pre-trained convolutional oriented boundary network to predict accurate boundaries from images, which are then used to construct high-quality region hierarchies for object detection and segmentation.
Journal ArticleDOI
Voxel- and graph-based point cloud segmentation of 3d scenes using perceptual grouping laws
TL;DR: This paper presents a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud.
References
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