A Robust and Reliable Point Cloud Recognition Network Under Rigid Transformation

Liu, Dongrui; Chen, Chuanchuan; Xu, Changqing; Cai, Qi; Chu, Lei; Wen, Fei; Qiu, Robert C.

doi:10.1109/tim.2022.3142077

Cited by 12 publications

(2 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and KPConv [74] are further proposed to enhance industrial applications based on the point cloud, including recognition [38], [41], [62], detection [19], [91], registration [34], [39], [66], [92], sampling [5], [30], [37], generation [6], [46], and interpretation [69].…”

Section: D Data Processingmentioning

confidence: 99%

Wavelet-based self-supervised learning for multi-scene image fusion

Liu

Qiao

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving. Despite their success, the underlying reasons for their astonishing generalization capabilities remain unclear. Our research addresses this gap by examining the generalization capabilities of NSFP through the lens of uniform stability, revealing that its performance is inversely proportional to the number of input point clouds. This finding sheds light on NSFP's effectiveness in handling large-scale point cloud scene flow estimation tasks. Motivated by such theoretical insights, we further explore the improvement of scene flow estimation by leveraging historical point clouds across multiple frames, which inherently increases the number of point clouds. Consequently, we propose a simple and effective method for multi-frame point cloud scene flow estimation, along with a theoretical evaluation of its generalization abilities. Our analysis confirms that the proposed method maintains a limited generalization error, suggesting that adding multiple frames to the scene flow optimization process does not detract from its generalizability. Extensive experimental results on large-scale autonomous driving Waymo Open and Argoverse lidar datasets demonstrate that the proposed method achieves state-of-the-art performance.

show abstract

Section: D Data Processingmentioning

confidence: 99%

Wavelet-based self-supervised learning for multi-scene image fusion

Liu

Qiao

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…It dramatically improved the computation speed. Liu et al proposed the self-contour-based transformation (SCT) method [15]. They proposed contour-awaretransformation that provides effective rotation and shift invariance.…”

Section: Introductionmentioning

confidence: 99%

LiDAR Point Cloud Object Recognition Method via Intensity Image Compensation

et al. 2023

View full text Add to dashboard Cite

LiDAR point cloud object recognition plays an important role in robotics, remote sensing, and automatic driving. However, it is difficult to fully represent the object feature information only by using the point cloud information. To address this challenge, we proposed a point cloud object recognition method that uses intensity image compensation, which is highly descriptive and computationally efficient. First, we constructed the local reference frame for the point cloud. Second, we proposed a method to calculate the deviation angle between the normal vector and local reference frame in the local neighborhood of the point cloud. Third, we extracted the contour information of the object from the intensity image corresponding to the point cloud, carried out Discrete Fourier Transform on the distance sequence between the barycenter of the contour and each point of the contour, and took the obtained result as Discrete Fourier Transform contour feature of the object. Finally, we repeated the above steps for the existing prior data and marked the obtained results as the feature information of the corresponding object to build a model library. We can recognize an unknown object by calculating the feature information of the object to be recognized and matching the feature information with the model library. We rigorously tested the proposed method with avalanche photon diode array LiDAR data and compared the results with those of four other methods. The experimental results show that the proposed method is superior to the comparison method in terms of description and computational efficiency and that it can meet the needs of practical applications.

show abstract

Rotation invariance and equivariance in 3D deep learning: a survey

Fei,

Deng

2024

Artif Intell Rev

View full text Add to dashboard Cite

Deep neural networks (DNNs) in 3D scenes show a strong capability of extracting high-level semantic features and significantly promote research in the 3D field. 3D shapes and scenes often exhibit complicated transformation symmetries, where rotation is a challenging and necessary subject. To this end, many rotation invariant and equivariant methods have been proposed. In this survey, we systematically organize and comprehensively overview all methods. First, we rewrite the previous definition of rotation invariance and equivariance by classifying them into weak and strong categories. Second, we provide a unified theoretical framework to analyze these methods, especially weak rotation invariant and equivariant ones that are seldom analyzed theoretically. We then divide existing methods into two main categories, i.e., rotation invariant ones and rotation equivariant ones, which are further subclassified in terms of manipulating input ways and basic equivariant block structures, respectively. In each subcategory, their common essence is highlighted, a couple of representative methods are analyzed, and insightful comments on their pros and cons are given. Furthermore, we deliver a general overview of relevant applications and datasets for two popular tasks of 3D semantic understanding and molecule-related. Finally, we provide several open problems and future research directions based on challenges and difficulties in ongoing research.

show abstract

A Robust and Reliable Point Cloud Recognition Network Under Rigid Transformation

Cited by 12 publications

References 49 publications

Wavelet-based self-supervised learning for multi-scene image fusion

Wavelet-based self-supervised learning for multi-scene image fusion

LiDAR Point Cloud Object Recognition Method via Intensity Image Compensation

Rotation invariance and equivariance in 3D deep learning: a survey

Contact Info

Product

Resources

About