Composite convolution: A flexible operator for deep learning on 3D point clouds-Reference-Cited by-同舟云学术

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Composite convolution: A flexible operator for deep learning on 3D point clouds

Published:2024-09 Issue: Volume:153 Page:110557
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Floris Alberto,Frittoli Luca^ORCID,Carrera Diego,Boracchi Giacomo

Publisher

Elsevier BV

Reference48 articles.

1. 3D object detection for autonomous driving: A survey;Qian;Pattern Recognit.,2022

2. One-class classification of airborne LiDAR data in urban areas using a presence and background learning algorithm;Ao;Remote Sens.,2017

3. DLA-Net: Learning dual local attention features for semantic segmentation of large-scale building facade point clouds;Su;Pattern Recognit.,2022

4. Deep open-set recognition for silicon wafer production monitoring;Frittoli;Pattern Recognit.,2022

5. DeepSIR: Deep semantic iterative registration for LiDAR point clouds;Li;Pattern Recognit.,2023

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