Results 51 to 60 of about 61,541 (298)
Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
Applied Sciences, 2023 Point cloud deep learning networks have been widely applied in point cloud classification, part segmentation and semantic segmentation. However, current point cloud deep learning networks are insufficient in the local feature extraction of the point ...
Xianquan Han +4 more
doaj +1 more source
BPNet: Bézier Primitive Segmentation on 3D Point Clouds
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023 This paper proposes BPNet, a novel end-to-end deep learning framework to learn Bézier primitive segmentation on 3D point clouds. The existing works treat different primitive types separately, thus limiting them to finite shape categories. To address this issue, we seek a generalized primitive segmentation on point clouds. Taking inspiration from Bézier
Fu, Rao +4 more
openaire +2 more sources
IEEE Access, 2020 Efficient semantic segmentation of large-scale 3D point clouds is a fundamental and essential capability for real-time intelligent systems, such as autonomous driving and augmented reality.
Jian Li +5 more
doaj +1 more source
Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation
IEEE Access, 2021 Accurate semantic segmentation of unstructured 3D point clouds requires large amount of annotated training data for deep learning. However, there is currently no free specialized software available that can efficiently annotate large 3D point clouds.
Muhammad Ibrahim +3 more
doaj +1 more source
Dynamic Convolution for 3D Point Cloud Instance Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 We propose an approach to instance segmentation from 3D point clouds based on dynamic convolution. This enables it to adapt, at inference, to varying feature and object scales. Doing so avoids some pitfalls of bottom up approaches, including a dependence on hyper-parameter tuning and heuristic post-processing pipelines to compensate for the inevitable ...
Tong He 0001 +2 more
openaire +4 more sources
A pothole detection method based on 3D point cloud segmentation
International Conference on Digital Image Processing, 2020 Road potholes affect comfort, safety, traffic condition and vehicle stability. Accurately detecting these potholes is vital for assessing the degree of pavement distress and developing road maintenance plan accordingly.
Ying Du +6 more
semanticscholar +1 more source
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds.
Weiyue Wang +3 more
semanticscholar +1 more source
Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation [PDF]
arXiv.org, 2023 Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervised semantic segmentation methods with application domains such as ...
Li Li, Hubert P. H. Shum, T. Breckon
semanticscholar +1 more source
Deep Semantic Segmentation of 3D Plant Point Clouds
, 2021 Plant phenotyping is an essential step in the plant breeding cycle, necessary to ensure food safety for a growing world population. Standard procedures for evaluating three-dimensional plant morphology and extracting relevant phenotypic characteristics are slow, costly, and in need of automation. Previous work towards automatic semantic segmentation of
Karoline Heiwolt +2 more
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GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 CVPR 2023.
Zihui Zhang +3 more
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