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Register a new userPointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
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Added by
Xuyang Bai Mr.
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning methods in this field, spatial consistency, which is essentially established by a Euclidean transformation between point clouds, has received almost no individual attention in existing learning frameworks. In this paper, we present PointDSC, a novel deep neural network that explicitly incorporates spatial consistency for pruning outlier correspondences. First, we propose a nonlocal feature aggregation module, weighted by both feature and spatial coherence, for feature embedding of the input correspondences. Second, we formulate a differentiable spectral matching module, supervised by pairwise spatial compatibility, to estimate the inlier confidence of each correspondence from the embedded features. With modest computation cost, our method outperforms the state-of-the-art hand-crafted and learning-based outlier rejection approaches on several real-world datasets by a significant margin. We also show its wide applicability by combining PointDSC with different 3D local descriptors.
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The performance of surface registration relies heavily on the metric used for the alignment error between the source and target shapes. Traditionally, such a metric is based on the point-to-point or point-to-plane distance from the points on the source surface to their closest points on the target surface, which is susceptible to failure due to instability of the closest-point correspondence. In this paper, we propose a novel metric based on the intersection points between the two shapes and a random straight line, which does not assume a specific correspondence. We verify the effectiveness of this metric by extensive experiments, including its direct optimization for a single registration problem as well as unsupervised learning for a set of registration problems. The results demonstrate that the algorithms utilizing our proposed metric outperforms the state-of-the-art optimization-based and unsupervised learning-based methods.
In this paper, we propose a novel minimum gravitational potential energy (MPE)-based algorithm for global point set registration. The feature descriptors extraction algorithms have emerged as the standard approach to align point sets in the past few decades. However, the alignment can be challenging to take effect when the point set suffers from raw point data problems such as noises (Gaussian and Uniformly). Different from the most existing point set registration methods which usually extract the descriptors to find correspondences between point sets, our proposed MPE alignment method is able to handle large scale raw data offset without depending on traditional descriptors extraction, whether for the local or global registration methods. We decompose the solution into a global optimal convex approximation and the fast descent process to a local minimum. For the approximation step, the proposed minimum potential energy (MPE) approach consists of two main steps. Firstly, according to the construction of the force traction operator, we could simply compute the position of the potential energy minimum; Secondly, with respect to the finding of the MPE point, we propose a new theory that employs the two flags to observe the status of the registration procedure. The method of fast descent process to the minimum that we employed is the iterative closest point algorithm; it can achieve the global minimum. We demonstrate the performance of the proposed algorithm on synthetic data as well as on real data. The proposed method outperforms the other global methods in terms of both efficiency, accuracy and noise resistance.
In this work, we propose UPDesc, an unsupervised method to learn point descriptors for robust point cloud registration. Our work builds upon a recent supervised 3D CNN-based descriptor extraction framework, namely, 3DSmoothNet, which leverages a voxel-based representation to parameterize the surrounding geometry of interest points. Instead of using a predefined fixed-size local support in voxelization, which potentially limits the access of richer local geometry information, we propose to learn the support size in a data-driven manner. To this end, we design a differentiable voxelization module that can back-propagate gradients to the support size optimization. To optimize descriptor similarity, the prior 3D CNN work and other supervised methods require abundant correspondence labels or pose annotations of point clouds for crafting metric learning losses. Differently, we show that unsupervised learning of descriptor similarity can be achieved by performing geometric registration in networks. Our learning objectives consider descriptor similarity both across and within point clouds without supervision. Through extensive experiments on point cloud registration benchmarks, we show that our learned descriptors yield superior performance over existing unsupervised methods.
Point cloud registration is the task of estimating the rigid transformation that aligns a pair of point cloud fragments. We present an efficient and robust framework for pairwise registration of real-world 3D scans, leveraging Hough voting in the 6D transformation parameter space. First, deep geometric features are extracted from a point cloud pair to compute putative correspondences. We then construct a set of triplets of correspondences to cast votes on the 6D Hough space, representing the transformation parameters in sparse tensors. Next, a fully convolutional refinement module is applied to refine the noisy votes. Finally, we identify the consensus among the correspondences from the Hough space, which we use to predict our final transformation parameters. Our method outperforms state-of-the-art methods on 3DMatch and 3DLoMatch benchmarks while achieving comparable performance on KITTI odometry dataset. We further demonstrate the generalizability of our approach by setting a new state-of-the-art on ICL-NUIM dataset, where we integrate our module into a multi-way registration pipeline.
Aligning partial views of a scene into a single whole is essential to understanding ones environment and is a key component of numerous robotics tasks such as SLAM and SfM. Recent approaches have proposed end-to-end systems that can outperform traditional methods by leveraging pose supervision. However, with the rising prevalence of cameras with depth sensors, we can expect a new stream of raw RGB-D data without the annotations needed for supervision. We propose UnsupervisedR&R: an end-to-end unsupervised approach to learning point cloud registration from raw RGB-D video. The key idea is to leverage differentiable alignment and rendering to enforce photometric and geometric consistency between frames. We evaluate our approach on indoor scene datasets and find that we outperform existing traditional approaches with classic and learned descriptors while being competitive with supervised geometric point cloud registration approaches.
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