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Stratified Transformer for 3D Point Cloud Segmentation [PDF]
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies.
Xin Lai +7 more
semanticscholar +4 more sources
Prototype Adaption and Projection for Few- and Zero-Shot 3D Point Cloud Semantic Segmentation [PDF]
IEEE Transactions on Image Processing, 2023 In this work, we address the challenging task of few-shot and zero-shot 3D point cloud semantic segmentation. The success of few-shot semantic segmentation in 2D computer vision is mainly driven by the pre-training on large-scale datasets like imagenet ...
Xudong Jiang, Wei Jiang, Henghui Ding
exaly +2 more sources
Few-shot 3D Point Cloud Semantic Segmentation [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Many existing approaches for 3D point cloud semantic segmentation are fully supervised. These fully supervised approaches heavily rely on large amounts of labeled training data that are difficult to obtain and cannot segment new classes after training ...
Na Zhao, Tat-Seng Chua, Gim Hee Lee
semanticscholar +4 more sources
Comprehensive review on 3D point cloud segmentation in plants
Artificial Intelligence in AgricultureSegmentation of three-dimensional (3D) point clouds is fundamental in comprehending unstructured structural and morphological data. It plays a critical role in research related to plant phenomics, 3D plant modeling, and functional-structural plant ...
Hongli Song +3 more
doaj +2 more sources
Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review
Remote Sensing, 2023 In the cultural heritage field, point clouds, as important raw data of geomatics, are not only three-dimensional (3D) spatial presentations of 3D objects but they also have the potential to gradually advance towards an intelligent data structure with ...
Su Yang, Miaole Hou, Songnian Li
doaj +3 more sources
Fast 3D point-cloud segmentation for interactive surfaces [PDF]
Companion Proceedings of the 2021 Conference on Interactive Surfaces and Spaces, 2021 Easily accessible depth sensors have enabled using point-cloud data to augment tabletop surfaces in everyday environments. However, point-cloud operations are computationally expensive and challenging to perform in real-time, particularly when targeting ...
E. Mthunzi +2 more
semanticscholar +3 more sources
A Review of Deep Learning-Based Semantic Segmentation for Point Cloud
IEEE Access, 2019 In recent years, the popularity of depth sensors and 3D scanners has led to a rapid development of 3D point clouds. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of researchers.
Jiaying Zhang +3 more
doaj +3 more sources
Semantic Segmentation Method for Sparse Point Clouds Based on Straight Flow Completion and Multi-Feature Fusion [PDF]
SensorsPoint cloud semantic segmentation is a vital task in 3D computer vision. However, the inherent sparsity of point clouds complicates the segmentation process.
Tong Zheng +4 more
doaj +2 more sources
3D point cloud segmentation oriented to the analysis of interactions [PDF]
2016 24th European Signal Processing Conference (EUSIPCO), 2016 Given the widespread availability of point cloud data from consumer depth sensors, 3D point cloud segmentation becomes a promising building block for high level applications such as scene understanding and interaction analysis.
Xiao Lin, J. Casas, M. Pardàs
semanticscholar +3 more sources
Point mask transformer for outdoor point cloud semantic segmentation
Computational Visual MediaCurrent outdoor point-cloud segmentation methods typically formulate semantic segmentation as a per-point/voxel-classification task. Although this strategy is straightforward because it classifies each point directly, it ignores the overall relationship ...
Xiangqian Li +4 more
doaj +3 more sources