A review of multimodal image matching: Methods and applications

Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning techniques 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. [code release]

/pdf/pointdsc-robust-point-cloud-registration-using-deep-spatial-54unzve3ig.pdf

PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency

Higher dimensional data such as video and 3D are the leading edge of multimedia retrieval and computer vision research. In this survey, we give a comprehensive overview and key insights into the state of the art of higher dimensional features from deep learning and also traditional approaches. Current approaches are frequently using 3D information from the sensor or are using 3D in modeling and understanding the 3D world. With the growth of prevalent application areas such as 3D games, self-driving automobiles, health monitoring and sports activity training, a wide variety of new sensors have allowed researchers to develop feature description models beyond 2D. Although higher dimensional data enhance the performance of methods on numerous tasks, they can also introduce new challenges and problems. The higher dimensionality of the data often leads to more complicated structures which present additional problems in both extracting meaningful content and in adapting it for current machine learning algorithms. Due to the major importance of the evaluation process, we also present an overview of the current datasets and benchmarks. Moreover, based on more than 330 papers from this study, we present the major challenges and future directions.

/pdf/a-survey-of-traditional-and-deep-learning-based-feature-2227yohdyx.pdf

A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision

Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with automated workflows presenting the main challenges. In this paper, we propose a voxel-based feature engineering that better characterize point clusters and provide strong support to supervised or unsupervised classification. We provide different feature generalization levels to permit interoperable frameworks. First, we recommend a shape-based feature set (SF1) that only leverages the raw X, Y, Z attributes of any point cloud. Afterwards, we derive relationship and topology between voxel entities to obtain a three-dimensional (3D) structural connectivity feature set (SF2). Finally, we provide a knowledge-based decision tree to permit infrastructure-related classification. We study SF1/SF2 synergy on a new semantic segmentation framework for the constitution of a higher semantic representation of point clouds in relevant clusters. Finally, we benchmark the approach against novel and best-performing deep-learning methods while using the full S3DIS dataset. We highlight good performances, easy-integration, and high F1-score (> 85%) for planar-dominant classes that are comparable to state-of-the-art deep learning.

Voxel-Based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods

Local voxelized structure for 3D binary feature representation and robust registration of point clouds from low-cost sensors

This article presents an efficient and robust estimator called compatibility-guided sampling consensus (CG-SAC) to achieve accurate 3-D point cloud registration. For correspondence-based registration methods, the random sample consensus (RANSAC) is served as a de facto solution for rigid transformation estimation from a number of feature correspondences. Unfortunately, RANSAC still suffers from two major limitations. First, it generates a hypothesis with at least three samples and desires a very large number of iterations to attain reasonable results, making it relatively time consuming. Second, the randomness during sampling can result in inaccurate results as it is highly potential to miss the optimal hypothesis. To solve these problems, we propose a compatibility-guided sampling strategy to eliminate randomness during sampling. In particular, only two correspondences are required by our method for hypothesis generation. We then rank correspondence pairs according to their compatibility scores because compatible correspondences are more likely to be correct and can yield more reasonable hypotheses. In addition, we propose a new geometric constraint named the distance between salient points (DSP) to measure the compatibility of two correspondences. Experiments on a set of real-world point cloud data with different application contexts and data modalities confirm the effectiveness of the proposed method. Comparison with several state-of-the-art estimators demonstrates the overall superiority of our CG-SAC estimator with regards to precision and time efficiency.

Compatibility-Guided Sampling Consensus for 3-D Point Cloud Registration

Multi-scale decomposition based fusion of infrared and visible image via total variation and saliency analysis

A grayscale weight with window algorithm for infrared and visible image registration

Local geometric descriptors act as an essential component for 3D rigid data matching. A rotational invariant local geometric descriptor usually consists of two components: local reference frame (LRF) and feature representation. However, existing evaluation efforts have mainly been paid on the LRF or the overall descriptor and the quantitative comparison of feature representations remains unexplored. This paper fills the gap by comprehensively evaluating nine state-of-the-art local geometric feature representations. In particular, our evaluation assesses feature representations based on ground-truth LRFs such that the ranking of tested methods is more convincing as compared with existing studies. The experiments are deployed on six standard datasets with various application scenarios (shape retrieval, point cloud registration, and object recognition) and data modalities (LiDAR, Kinect, and Space Time) as well as perturbations including Gaussian noise, shot noise, data decimation, clutter, occlusion, and limited overlap. The evaluated terms cover the major concerns for a feature representation, e.g., distinctiveness, robustness, compactness, and efficiency. The outcomes present interesting findings that may shed new light on this community and provide complementary perspectives to existing evaluations on the topic of local geometric feature description. A summary of evaluated methods regarding their peculiarities is finally presented to guide real-world applications and new descriptor crafting.

Siwen Quan

Papers

Local voxelized structure for 3D binary feature representation and robust registration of point clouds from low-cost sensors

Compatibility-Guided Sampling Consensus for 3-D Point Cloud Registration

Multi-scale decomposition based fusion of infrared and visible image via total variation and saliency analysis

A grayscale weight with window algorithm for infrared and visible image registration

Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching