In this paper, we propose a new redundant wavelet transform applicable to scalar functions defined on high dimensional coordinates, weighted graphs and networks. The proposed transform utilizes the distances between the given data points. We modify the filter-bank decomposition scheme of the redundant wavelet transform by adding in each decomposition level linear operators that reorder the approximation coefficients. These reordering operators are derived by organizing the tree-node features so as to shorten the path that passes through these points. We explore the use of the proposed transform to image denoising, and show that it achieves denoising results that are close to those obtained with the BM3D algorithm.
Northwestern Polytechnical University（西北工业大学）；Harbin Institute of Technology（哈尔滨工业大学）；Peng Cheng Laboratory（鹏城实验室）；University of Macau（澳门大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）
Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging depth of designed networks to obtain better denoising performance, which may cause training difficulty. In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB). DCB uses a dynamic convolution to dynamically adjust parameters of sever…
RVS College of Engineering and Technology（RVS工程技术学院）
In recent years, many research achievements are made in the medical image fusion field. Fusion is basically extraction of best of inputs and conveying it to the output. Medical Image fusion means that several of various modality image information is comprehended together to form one image to express its information. The aim of image fusion is to integrate complementary and redundant information. In this paper, a multimodal image fusion algorithm based on the dual-tree complex wavelet transform (DT-CWT) and adaptive particle swarm optimization (APSO) is proposed. Fusion is achieved through the …
Multiview super-resolution image reconstruction (SRIR) is often cast as a resampling problem by merging non-redundant data from multiple low-resolution (LR) images on a finer high-resolution (HR) grid, while inverting the effect of the camera point spread function (PSF). One main problem with multiview methods is that resampling from nonuniform samples (provided by LR images) and the inversion of the PSF are highly nonlinear and ill-posed problems. Non-linearity and ill-posedness are typically overcome by linearization and regularization, often through an iterative optimization process, which …
PWAVEP: Purifying Imperceptible Adversarial Perturbations in 3D Point Clouds via Spectral Graph Wavelets
Software College, Northeastern University（东北大学软件学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；Software College, Shenyang University of Technology（沈阳理工大学软件学院）
With recent success of deep learning in 2D visual recognition, deep learning-based 3D point cloud analysis has received increasing attention from the community, especially due to the rapid development of autonomous driving technologies. However, most existing methods directly learn point features in the spatial domain, leaving the local structures in the spectral domain poorly investigated. In this paper, we introduce a new method, PointWavelet, to explore local graphs in the spectral domain via a learnable graph wavelet transform. Specifically, we first introduce the graph wavelet transform t…
Cloud detection is a specialized application of image recognition and object detection using remotely sensed data. The task presents a number of challenges, including analyzing images obtained in visible, infrared and multi-spectral frequencies, usually without ground truth data for comparison. Moreover, machine learning and deep learning (MLDL) algorithms applied to this task are required to be computationally efficient, as they are typically deployed in low-power devices and called to operate in real-time. This paper explains Wavelet Transform (WT) theory, comparing it to more widely used im…
Wavelet-based Global Orientation and Surface Reconstruction for Point Clouds
Department of Mathematical Sciences, Tsinghua University, Beijing, China（清华大学数学科学系）；Yau Mathematical Sciences Center, Tsinghua University, Beijing, China（清华大学姚式数学科学中心）；School of Software, Tsinghua University, Beijing, China（清华大学软件学院）；School of Mathematical Sciences, University of Science and Technology of China, Hefei（中国科学技术大学数学科学学院）；Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, China（北京应用数学科学研究所）
RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network
NYU Tandon School of Engineering, New York University（纽约大学坦登工程学院）；NYUAD Center for Artificial Intelligence and Robotics, New York University Abu Dhabi（纽约大学阿布扎比分校人工智能与机器人中心）
Wavelet-Driven Masked Image Modeling: A Path to Efficient Visual Representation
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Peng Cheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Jiao Tong University（上海交通大学）
Universidad Técnica Federico Santa María（费德里科·圣玛丽亚技术大学）
The intrinsically hierarchical and blended structure of interstellar molecular clouds, plus the always increasing resolution of astronomical instruments, demand advanced and automated pattern recognition techniques for identifying and connecting source components in spectroscopic cubes. We extend the work done in multiresolution analysis using Wavelets for astronomical 2D images to 3D spectroscopic cubes, combining the results with the Dendrograms approach to offer a hierarchical representation of connections between sources at different scale levels. We test our approach in real data from the…
Graph Convolutional Networks (GCNs) are widely used in a variety of applications, and can be seen as an unstructured version of standard Convolutional Neural Networks (CNNs). As in CNNs, the computational cost of GCNs for large input graphs (such as large point clouds or meshes) can be high and inhibit the use of these networks, especially in environments with low computational resources. To ease these costs, quantization can be applied to GCNs. However, aggressive quantization of the feature maps can lead to a significant degradation in performance. On a different note, Haar wavelet transform…
We propose a method for speeding up a 3D point cloud registration through a cascading feature extraction. The current approach with the highest accuracy is realized by iteratively executing feature extraction and registration using deep features. However, iterative feature extraction takes time. Our proposed method significantly reduces the computational cost using cascading shallow layers. Our idea is to omit redundant computations that do not always contribute to the final accuracy. The proposed approach is approximately three times faster than the existing methods without a loss of accuracy…
PointJEM: Self-supervised Point Cloud Understanding for Reducing Feature Redundancy via Joint Entropy Maximization
School of Information Science and Technology, Northwest University（西北大学信息科学与技术学院）；National and Local Joint Engineering Research Center for Cultural Heritage Digitization（文化遗产数字化国家地方联合工程研究中心）
Dense 3D visual mapping estimates as many as possible pixel depths, for each image. This results in very dense point clouds that often contain redundant and noisy information, especially for surfaces that are roughly planar, for instance, the ground or the walls in the scene. In this paper we leverage on semantic image segmentation to discriminate which regions of the scene require simplification and which should be kept at high level of details. We propose four different point cloud simplification methods which decimate the perceived point cloud by relying on class-specific local and global s…
Nanjing University of Science and Technology（南京理工大学）；Nanyang Technological University（南洋理工大学）
Point cloud segmentation is a fundamental visual understanding task in 3D vision. A fully supervised point cloud segmentation network often requires a large amount of data with point-wise annotations, which is expensive to obtain. In this work, we present the Compositional Prototype Network that can undertake point cloud segmentation with only a few labeled training data. Inspired by the few-shot learning literature in images, our network directly transfers label information from the limited training data to unlabeled test data for prediction. The network decomposes the representations of comp…
Nanjing University（南京大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
The growing size of point clouds enlarges consumptions of storage, transmission, and computation of 3D scenes. Raw data is redundant, noisy, and non-uniform. Therefore, simplifying point clouds for achieving compact, clean, and uniform points is becoming increasingly important for 3D vision and graphics tasks. Previous learning based methods aim to generate fewer points for scene understanding, regardless of the quality of surface reconstruction, leading to results with low reconstruction accuracy and bad point distribution. In this paper, we propose a novel point cloud simplification network …
Central South University（中南大学）；Zhejiang Lab（之江实验室）
Data augmentation is an effective regularization strategy for mitigating overfitting in deep neural networks, and it plays a crucial role in 3D vision tasks, where the point cloud data is relatively limited. While mixing-based augmentation has shown promise for point clouds, previous methods mix point clouds either on block level or point level, which has constrained their ability to strike a balance between generating diverse training samples and preserving the local characteristics of point clouds. Additionally, the varying importance of each part of the point clouds has not been fully consi…
The curve skeleton is an important shape descriptor that has been utilized in various applications in computer graphics, machine vision, and artificial intelligence. In this study, the endpoint-based part-aware curve skeleton (EPCS) extraction method for low-quality point clouds is proposed. The novel random center shift (RCS) method is first proposed for detecting the endpoints on point clouds. The endpoints are used as the initial seed points for dividing each part into layers, and then the skeletal points are obtained by computing the center points of the oriented bounding box (OBB) of the …
Efficient 3D Perception on Multi-Sweep Point Cloud with Gumbel Spatial Pruning
Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Department of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）；Noah’s Ark Lab（诺亚实验室）；Department of Computer Science, University of Hong Kong（香港大学计算机科学系）
Technical University of Munich（慕尼黑工业大学）；Google（谷歌）
This work proposes a general-purpose, fully-convolutional network architecture for efficiently processing large-scale 3D data. One striking characteristic of our approach is its ability to process unorganized 3D representations such as point clouds as input, then transforming them internally to ordered structures to be processed via 3D convolutions. In contrast to conventional approaches that maintain either unorganized or organized representations, from input to output, our approach has the advantage of operating on memory efficient input data representations while at the same time exploiting…
Department of Mechanical Engineering, State University of New York, Korea, Incheon, South Korea（纽约州立大学机械工程系）；Department of Mechanical Engineering, State University of New York, Stony Brook, NY, United States（纽约州立大学石溪分校机械工程系）；Department of Computer Science, State University of New York, Korea, Incheon, South Korea（纽约州立大学计算机科学系）；Department of Computer Science, State University of New York, Stony Brook, NY, United States（纽约州立大学石溪分校计算机科学系）
Existing single-stage detectors for locating objects in point clouds often treat object localization and category classification as separate tasks, so the localization accuracy and classification confidence may not well align. To address this issue, we present a new single-stage detector named the Confident IoU-Aware Single-Stage object Detector (CIA-SSD). First, we design the lightweight Spatial-Semantic Feature Aggregation module to adaptively fuse high-level abstract semantic features and low-level spatial features for accurate predictions of bounding boxes and classification confidence. Al…
Aggregating neighbor features is essential for point cloud classification. In the existing work, each point in the cloud may inevitably be selected as the neighbors of multiple aggregation centers, as all centers will gather neighbor features from the whole point cloud independently. Thus each point has to participate in the calculation repeatedly and generates redundant duplicates in the memory, leading to intensive computation costs and memory consumption. Meanwhile, to pursue higher accuracy, previous methods often rely on a complex local aggregator to extract fine geometric representation,…
The University of Tokyo（东京大学）；National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所）
We propose a novel and efficient representation for single-view depth estimation using Convolutional Neural Networks (CNNs). Point-cloud is generally used for CNN-based 3D scene reconstruction; however it has some drawbacks: (1) it is redundant as a representation for planar surfaces, and (2) no spatial relationships between points are available (e.g, texture and surface). As a more efficient representation, we introduce a triangular-patch-cloud, which represents the surface of the 3D structure using a set of triangular patches, and propose a CNN framework for its 3D structure estimation. In o…
Point cloud sequences are commonly used to accurately detect 3D objects in applications such as autonomous driving. Current top-performing multi-frame detectors mostly follow a Detect-and-Fuse framework, which extracts features from each frame of the sequence and fuses them to detect the objects in the current frame. However, this inevitably leads to redundant computation since adjacent frames are highly correlated. In this paper, we propose an efficient Motion-guided Sequential Fusion (MSF) method, which exploits the continuity of object motion to mine useful sequential contexts for object de…
PillarTrack:Boosting Pillar Representation for Transformer-based 3D Single Object Tracking on Point Clouds
School of Mathematics and Computer Science, Nanchang University（南昌大学数学与计算机科学学院）；School of Automation, Southeast University（东南大学自动化学院）；Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University（华南师范大学 Aberdeen 数据科学与人工智能研究院）；Houmo AI
The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Southern University of Science and Technology（南方科技大学）
Compared with the visual grounding on 2D images, the natural-language-guided 3D object localization on point clouds is more challenging. In this paper, we propose a new model, named InstanceRefer, to achieve a superior 3D visual grounding through the grounding-by-matching strategy. In practice, our model first predicts the target category from the language descriptions using a simple language classification model. Then, based on the category, our model sifts out a small number of instance candidates (usually less than 20) from the panoptic segmentation of point clouds. Thus, the non-trivial 3D…
SRENet: Spectral Re-Entry Network for Point Cloud Action Recognition
School of Software Engineering, South China University of Technology（南方科技大学软件工程学院）；School of Automation Science and Engineering, South China University of Technology（南方科技大学自动化科学与工程学院）；School of Computer Science, University of Sydney（悉尼大学计算机科学学院）；School of Science, Edith Cowan University（爱丁堡牛津大学科学学院）
Universidad Nacional de San Agustín（圣奥古斯丁国立大学）；Department of Computer Science, University of Chile（智利大学计算机科学系）；Universidad La Salle（拉萨尔大学）
Point cloud completion is the task of predicting complete geometry from partial observations using a point set representation for a 3D shape. Previous approaches propose neural networks to directly estimate the whole point cloud through encoder-decoder models fed by the incomplete point set. By predicting the complete model, the current methods compute redundant information because the output also contains the known incomplete input geometry. This paper proposes an end-to-end neural network architecture that focuses on computing the missing geometry and merging the known input and the predicte…
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学智能系统与控制研究所）
In this paper, we propose PASS3D to achieve point-wise semantic segmentation for 3D point cloud. Our framework combines the efficiency of traditional geometric methods with robustness of deep learning methods, consisting of two stages: At stage-1, our accelerated cluster proposal algorithm will generate refined cluster proposals by segmenting point clouds without ground, capable of generating less redundant proposals with higher recall in an extremely short time; stage-2 we will amplify and further process these proposals by a neural network to estimate semantic label for each point and meanwh…
Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Microsoft Research（微软研究院）
This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames are similar, motion estimation is key to effective compression of these sequences. It however remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the points clouds as signals on the vertices of the grap…
Birla Institute of Technology and Science, Pilani（皮拉尼比尔拉理工学院）；SIMTech, Agency for Science, Technology and Research（新加坡科技研究局制造技术研究所）；National University of Singapore（新加坡国立大学）
We present a neural point cloud rendering pipeline through a novel multi-frequency-aware patch adversarial learning framework. The proposed approach aims to improve the rendering realness by minimizing the spectrum discrepancy between real and synthesized images, especially on the high-frequency localized sharpness information which causes image blur visually. Specifically, a patch multi-discriminator scheme is proposed for the adversarial learning, which combines both spectral domain (Fourier Transform and Discrete Wavelet Transform) discriminators as well as the spatial (RGB) domain discrimi…
National Taiwan University（国立台湾大学）；Mobile Drive Technology（移动驱动科技公司）
Despite the success of deep learning on supervised point cloud semantic segmentation, obtaining large-scale point-by-point manual annotations is still a significant challenge. To reduce the huge annotation burden, we propose a Region-based and Diversity-aware Active Learning (ReDAL), a general framework for many deep learning approaches, aiming to automatically select only informative and diverse sub-scene regions for label acquisition. Observing that only a small portion of annotated regions are sufficient for 3D scene understanding with deep learning, we use softmax entropy, color discontinu…
CA-I2P: Channel-Adaptive Registration Network with Global Optimal Selection
School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）；Institute of Advanced Technology, University of Science and Technology of China（先进技术研究院，中国科学技术大学）
