Point cloud upsampling can improve the quality of the initial point cloud, significantly enhancing the performance of downstream tasks such as classification and segmentation. Existing methods mostly focus on generating the geometric details of point clouds, neglecting noise suppression. To address this, we propose a novel network based on a conditional diffusion model, incorporating the Adaptive Noise Suppression (ANS) module, which we refer to as PDANS. The ANS module assigns weights to each point and determines the removal strategy based on these weights, reducing the impact of noisy points on the sampling process. The module first selects the neighborhood set for each point in the point cloud and performs a weighted sum between the point and its neighbors. It then adjusts the removal points based on the weighted sum, effectively mitigating the bias caused by outliers. We introduce the TreeTrans (TT) module to capture more correlated feature information. This module learns the interaction between high-level and low-level features, resulting in a more comprehensive and refined feature representation. Our results on several widely used benchmark datasets demonstrate that PDANS exhibits exceptional robustness in noisy point cloud processing and outperforms current state-of-the-art(SOTA) methods in terms of performance. Code is available at https://github.com/Baty2023/PDANS.

Cryptocurrencies represented by Bitcoin have fully demonstrated their advantages and great potential in payment and monetary systems during the last decade. The mining pool, which is considered the source of Bitcoin, is the cornerstone of market stability. The surveillance of the mining pool can help regulators effectively assess the overall health of Bitcoin and issues. However, the anonymity of mining-pool miners and the difficulty of analyzing large numbers of transactions limit in-depth analysis. It is also a challenge to achieve intuitive and comprehensive monitoring of multi-source heterogeneous data. In this study, we present SuPoolVisor, an interactive visual analytics system that supports surveillance of the mining pool and de-anonymization by visual reasoning. SuPoolVisor is divided into pool level and address level. At the pool level, we use a sorted stream graph to illustrate the evolution of computing power of pools over time, and glyphs are designed in two other views to demonstrate the influence scope of the mining pool and the migration of pool members. At the address level, we use a force-directed graph and a massive sequence view to present the dynamic address network in the mining pool. Particularly, these two views, together with the Radviz view, support an iterative visual reasoning process for de-anonymization of pool members and provide interactions for cross-view analysis and identity marking. Effectiveness and usability of SuPoolVisor are demonstrated using three cases, in which we cooperate closely with experts in this field.

In this paper, we present a robust global approach for point cloud registration from uniformly sampled points. Based on eigenvalues and normals computed from multiple scales, we design fast descriptors to extract local structures of these points. The eigenvalue-based descriptor is effective at finding seed matches with low precision using nearest neighbor search. Generally, recovering the transformation from matches with low precision is rather challenging. Therefore, we introduce a mechanism named correspondence propagation to aggregate each seed match into a set of numerous matches. With these sets of matches, multiple transformations between point clouds are computed. A quality function formulated from distance errors is used to identify the best transformation and fulfill a coarse alignment of the point clouds. Finally, we refine the alignment result with the trimmed iterative closest point algorithm. The proposed approach can be applied to register point clouds with significant or limited overlaps and small or large transformations. More encouragingly, it is rather efficient and very robust to noise. A comparison to traditional descriptor-based methods and other global algorithms demonstrates the fine performance of the proposed approach. We also show its promising application in large-scale reconstruction with the scans of two real scenes. In addition, the proposed approach can be used to register low-resolution point clouds captured by Kinect as well.

There lies a geometric relationship (homography) between two frames in the video sequences captured by pan-tilt (PT) cameras due to their constrained movement, and the geometric relationship is valuable for reducing the spatial redundancy in video coding. In this paper, we propose a novel homography-based search (HBS) algorithm for block motion estimation in coding the sequences captured by PT cameras, which well utilizes the homography between two frames. In addition, adaptive thresholds are adopted in our method to classify different kinds of blocks. Compared with other traditional fast algorithms, the proposed HBS algorithm is proved more efficient for the sequences captured by PT cameras.

An adaptive threshold for early termination is proposed in this paper to enhance the cross octagonal search algorithm (COSA). In block motion estimation, search patterns have a very important impact on searching speed and distortion performance. COSA achieves almost the same visual quality with full-search algorithm while it takes much fewer search points than hybrid unsymmetrical-cross multi-hexagon-grid search (UMHexagonS) indeed. An adaptive threshold for early termination is introduced to COSA which avoids meaningless calculation after the searching point is good enough. The simulation results show that the proposed method reduces the motion estimation time by from 10.90% to 42.75% than UMHexagonS according to different types of sequences with negligible coding loss.

The geometry of plane-based calibration methods is well understood, but some user interaction is often needed in practice for feature detection. This paper presents a fully automatic calibration system that uses patterns of pairs of concentric circles. The key observation is to introduce a geometric method that constructs a sequence of points strictly convergent to the image of the circle center from an arbitrary point. The method automatically detects the points of the pattern features by the construction method, and identify them by invariants. It then takes advantage of homological constraints to consistently and optimally estimate the features in the image. The experiments demonstrate the robustness and the accuracy of the new method.

This paper presents a novel and simple method of analyzing the motion of a large image sequence captured by a calibrated outward-looking video camera moving on a circular trajectory for large-scale environment applications. Previous circular motion algorithms mainly focus on inward-looking turntable-like setups. They are not suitable for outward-looking motion where the conic trajectory of corresponding points degenerates to straight lines. The circular motion of a calibrated camera essentially has only one unknown rotation angle for each frame. The motion recovery for the entire sequence computes only one fundamental matrix of a pair of frames to extract the angular motion of the pair using Laguerre's formula and then propagates the computation of the unknown rotation angles to the other frames by tracking one point over at least three frames. Finally, a maximum-likelihood estimation is developed for the optimization of the whole sequence. Extensive experiments demonstrate the validity of the method and the feasibility of the application in image-based rendering.

Circular motion or single axis motion is widely used in computer vision and graphics for 3D model acquisition. This paper describes a new and simple method for recovering the geometry of uncalibrated circular motion from a minimal set of only two points in four images. This problem has been previously solved using nonminimal data either by computing the fundamental matrix and trifocal tensor in three images or by fitting conics to tracked points in five or more images. It is first established that two sets of tracked points in different images under circular motion for two distinct space points are related by a homography. Then, we compute a plane homography from a minimal two points in four images. After that, we show that the unique pair of complex conjugate eigenvectors of this homography are the image of the circular points of the parallel planes of the circular motion. Subsequently, all other motion and structure parameters are computed from this homography in a straighforward manner. The experiments on real image sequences demonstrate the simplicity, accuracy, and robustness of the new method.

We describe a new and simple method of recovering the geometry of uncalibrated circular motion or single axis motion using a minimal data set of 2 points in 4 images. This problem has been solved using nonminimal data either by computing the fundamental matrix and trifocal tensor in 3 images, or by fitting conics to tracked points in 5 images. Our new method first computes a planar homography from a minimum of 2 points in 4 images. It is shown that two eigenvectors of this homography are the images of the circular points. Then, other fixed image entities and rotation angles can be straightforwardly computed. The crux of the method lies in relating this planar homography from two different points to a homology naturally induced by corresponding points on different conic loci from a circular motion. The experiments on real image sequences demonstrate the simplicity, accuracy and robustness of the new method.

Previous algorithms for recovering 3D geometry from an uncalibrated image sequence of a single axis motion of unknown rotation angles are mainly based on the computation of two-view fundamental matrices and three-view trifocal tensors. We propose three new methods that are based on fitting a conic locus to corresponding image points over multiple views. The main advantage is that determining only five parameters of a conic from one corresponding point over at least five views is simpler and more robust than determining a fundamental matrix from two views or a trifocal tensor from three views. It is shown that the geometry of single axis motion can be recovered either by computing one conic locus and one fundamental matrix or by computing at least two conic loci. A maximum likelihood solution based on this parametrization of the single axis motion is also described for optimal estimation using three or more loci. The experiments on real image sequences demonstrate the simplicity, accuracy, and robustness of the new methods.

In this paper, we propose a new approach for recovering 3D geometry from uncalibrated and unknown rotation angles of single axis motions. Unlike previous approaches, the computation of the trifocal tensor is not required. The new approach is based on fitting a conic to the corresponding points in different images of the same space point. It is then shown that the essential geometry of single axis motion is encoded by a conic. Since we are determining only 5 parameters from N views instead of 18 parameters from a subsequence of 3 views, our approach is much more simple and robust. The experiments on a real image sequence demonstrate the accuracy and robustness of the new algorithm.