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Global Context Aware Convolutions for 3D Point Cloud Understanding
Recent advances in deep learning for 3D point clouds have shown great pr...
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SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation
Point cloud analysis has drawn broader attentions due to its increasing ...
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A Rotation-Invariant Framework for Deep Point Cloud Analysis
Recently, many deep neural networks were designed to process 3D point cl...
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IEEE Signal Processing Letters
Deep neural networks have achieved great progress in 3D scene understand...
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Endowing Deep 3D Models with Rotation Invariance Based on Principal Component Analysis
In this paper, we propose a simple yet effective method to endow deep 3D...
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DeLiO: Decoupled LiDAR Odometry
Most LiDAR odometry algorithms estimate the transformation between two c...
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Point-cloud-based place recognition using CNN feature extraction
This paper proposes a novel point-cloud-based place recognition system t...
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A Self Contour-based Rotation and Translation-Invariant Transformation for Point Clouds Recognition
Recently, several direct processing point cloud models have achieved state-of-the-art performances for classification and segmentation tasks. However, these methods lack rotation robustness, and their performances degrade severely under random rotations, failing to extend to real-world applications with varying orientations. To address this problem, we propose a method named Self Contour-based Transformation (SCT), which can be flexibly integrated into a variety of existing point cloud recognition models against arbitrary rotations without any extra modifications. The SCT provides efficient and mathematically proved rotation and translation invariance by introducing Rotation and Translation-Invariant Transformation. It linearly transforms Cartesian coordinates of points to the self contour-based rotation-invariant representations while maintaining the global geometric structure. Moreover, to enhance discriminative feature extraction, the Frame Alignment module is further introduced, aiming to capture contours and transform self contour-based frames to the intra-class frame. Extensive experimental results and mathematical analyses show that the proposed method outperforms the state-of-the-art approaches under arbitrary rotations without any rotation augmentation on standard benchmarks, including ModelNet40, ScanObjectNN and ShapeNet.
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