PointCRA reduces information loss in deep point cloud networks by treating temporal trend variation as an extra evaluation dimension alongside spatial and channel attention, guided by a neighborhood homogeneity constraint.
Decoupled local aggregation for point cloud learning
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LIDARLearn is a unified PyTorch library integrating 29 supervised point cloud architectures, 7 self-supervised pre-training methods, and 5 PEFT strategies with built-in cross-validation, statistical testing, and automated reporting.
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Channel-Level Relation to Attentive Aggregation with Neighborhood-Homogeneity Constraint for Point Cloud Analysis
PointCRA reduces information loss in deep point cloud networks by treating temporal trend variation as an extra evaluation dimension alongside spatial and channel attention, guided by a neighborhood homogeneity constraint.
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LIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learning
LIDARLearn is a unified PyTorch library integrating 29 supervised point cloud architectures, 7 self-supervised pre-training methods, and 5 PEFT strategies with built-in cross-validation, statistical testing, and automated reporting.