Proceedings ArticleDOI
Fast Point Feature Histograms (FPFH) for 3D registration
Radu Bogdan Rusu,Nico Blodow,Michael Beetz +2 more
- pp 1848-1853
TLDR
This paper modifications their mathematical expressions and performs a rigorous analysis on their robustness and complexity for the problem of 3D registration for overlapping point cloud views, and proposes an algorithm for the online computation of FPFH features for realtime applications.Abstract:
In our recent work [1], [2], we proposed Point Feature Histograms (PFH) as robust multi-dimensional features which describe the local geometry around a point p for 3D point cloud datasets. In this paper, we modify their mathematical expressions and perform a rigorous analysis on their robustness and complexity for the problem of 3D registration for overlapping point cloud views. More concretely, we present several optimizations that reduce their computation times drastically by either caching previously computed values or by revising their theoretical formulations. The latter results in a new type of local features, called Fast Point Feature Histograms (FPFH), which retain most of the discriminative power of the PFH. Moreover, we propose an algorithm for the online computation of FPFH features for realtime applications. To validate our results we demonstrate their efficiency for 3D registration and propose a new sample consensus based method for bringing two datasets into the convergence basin of a local non-linear optimizer: SAC-IA (SAmple Consensus Initial Alignment).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.
Proceedings ArticleDOI
3D is here: Point Cloud Library (PCL)
Radu Bogdan Rusu,Steve Cousins +1 more
TL;DR: PCL (Point Cloud Library) is presented, an advanced and extensive approach to the subject of 3D perception that contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation.
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
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Yin Zhou,Oncel Tuzel +1 more
TL;DR: Zhou et al. as mentioned in this paper propose VoxelNet, a generic 3D detection network that unifies feature extraction and bounding box prediction into a single stage, end-to-end trainable deep network.
Proceedings ArticleDOI
PointConv: Deep Convolutional Networks on 3D Point Clouds
Wenxuan Wu,Zhongang Qi,Li Fuxin +2 more
TL;DR: The dynamic filter is extended to a new convolution operation, named PointConv, which can be applied on point clouds to build deep convolutional networks and is able to achieve state-of-the-art on challenging semantic segmentation benchmarks on 3D point clouds.
References
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Journal ArticleDOI
A method for registration of 3-D shapes
Paul J. Besl,H.D. McKay +1 more
TL;DR: In this paper, the authors describe a general-purpose representation-independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
Proceedings ArticleDOI
Efficient variants of the ICP algorithm
Szymon Rusinkiewicz,Marc Levoy +1 more
TL;DR: An implementation is demonstrated that is able to align two range images in a few tens of milliseconds, assuming a good initial guess, and has potential application to real-time 3D model acquisition and model-based tracking.
Journal ArticleDOI
Using spin images for efficient object recognition in cluttered 3D scenes
Andrew E. Johnson,Martial Hebert +1 more
TL;DR: In this paper, a 3D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion is presented, which is based on matching surfaces by matching points using the spin image representation.
Journal ArticleDOI
Iterative point matching for registration of free-form curves and surfaces
TL;DR: A heuristic method has been developed for registering two sets of 3-D curves obtained by using an edge-based stereo system, or two dense3-D maps obtained by use a correlation-based stereoscopic system, and it is efficient and robust, and yields an accurate motion estimate.
Iterative point matching for registration of free-form curves
TL;DR: In this article, a least-squares technique is used to estimate 3D motion from the point correspondences, which reduces the average distance between curves in two sets, and yields an accurate motion estimate.