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PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
[article]
2020
arXiv
pre-print
Preserved Fulltext
Instance segmentation is an important task for scene understanding. Compared to the fully-developed 2D, 3D instance segmentation for point clouds have much room to improve. ...
In this paper, we present PointGroup, a new end-to-end bottom-up architecture, specifically focused on better grouping the points by exploring the void space between objects. ...
. • We propose a bottom-up 3D instance segmentation framework, named PointGroup, to deal with the challenging 3D instance segmentation task. • We propose a point clustering method based on dual coordinate ...
arXiv:2004.01658v1
fatcat:atrq6kbukjf35mz3mipb7ffvcy
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Instance segmentation is an important task for scene understanding. Compared to the fully-developed 2D, 3D instance segmentation for point clouds have much room to improve. ...
In this paper, we present PointGroup, a new endto-end bottom-up architecture, specifically focused on better grouping the points by exploring the void space between objects. ...
. • We propose a bottom-up 3D instance segmentation framework, named PointGroup, to deal with the challenging 3D instance segmentation task. • We propose a point clustering method based on dual coordinate ...
doi:10.1109/cvpr42600.2020.00492
dblp:conf/cvpr/JiangZS0FJ20
fatcat:qexojukex5asdljhnni7m6bqum
HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor
[article]
2021
arXiv
pre-print
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After the 3D instance segmentation, we post-process the segmented point cloud by removing outliers and projecting all points onto a top-view 2D map representation. ...
To tackle these issues, we propose HIDA, a lightweight assistive system based on 3D point cloud instance segmentation with a solid-state LiDAR sensor, for holistic indoor detection and avoidance. ...
After that, the captured point cloud will be sent to the segmentation network for 3D instance segmentation.
3D Instance Segmentation In the data preprocessing stage, if the point cloud is too large, ...
arXiv:2107.03180v1
fatcat:ttzevemruvhavghufz4e2r6pky
PST: Plant Segmentation Transformer Enhanced Phenotyping of MLS Oilseed Rape Point Cloud
[article]
2022
arXiv
pre-print
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The results proved that PST and PST-PointGroup (PG) achieved state-of-the-art performance in semantic and instance segmentation tasks. ...
PST is composed of: (i) a dynamic voxel feature encoder (DVFE) to aggregate per point features with raw spatial resolution; (ii) dual window sets attention block to capture the contextual information; ...
Instance segmentation head in PointGroup This section briefly revisits the instance segmentation head in PG for completeness. ...
arXiv:2206.13082v1
fatcat:sxa5jtseofcvtks346wxhxvsae
Hierarchical Aggregation for 3D Instance Segmentation
[article]
2021
arXiv
pre-print
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for preliminarily clustering points to sets and set aggregation for generating complete instances from sets. ...
Instance segmentation on point clouds is a fundamental task in 3D scene perception. ...
PointGroup [20] proposes to cluster points based on dual coordinate sets and designs ScoreNet to predict scores for instances. ...
arXiv:2108.02350v1
fatcat:d6hvyhtfyvc4jkhcvo7lzeyryi
STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
[article]
2022
arXiv
pre-print
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Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large-scale data collection, sanitization ...
Motivated by this, we explore the procedurally synthetic 3D data generation paradigm to equip individuals with the full capability of creating large-scale annotated photogrammetry point clouds. ...
Note that different instances are displayed in different random colors. Best viewed in color.
: Pointgroup: Dual-set point grouping for 3d instance segmentation. ...
arXiv:2203.09065v1
fatcat:jjosk5xwsfhx7jguehkbveqip4
Sparse Cross-Scale Attention Network for Efficient LiDAR Panoptic Segmentation
2022
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Two major challenges of 3D LiDAR Panoptic Segmentation (PS) are that point clouds of an object are surface-aggregated and thus hard to model the long-range dependency especially for large instances, and ...
For the surface-aggregated points, SCAN adopts a novel sparse class-agnostic representation of instance centroids, which can not only maintain the sparsity of aligned features to solve the under-segmentation ...
PointGroup (Jiang et al. 2020) proposes a dual-clustering method to refine inaccurate offset predictions around object boundaries. ...
doi:10.1609/aaai.v36i3.20197
fatcat:d3ip43iix5appbzrwx52kclgtu
Sparse Cross-scale Attention Network for Efficient LiDAR Panoptic Segmentation
[article]
2022
arXiv
pre-print
Preserved Fulltext
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The file type is application/pdf.
Two major challenges of 3D LiDAR Panoptic Segmentation (PS) are that point clouds of an object are surface-aggregated and thus hard to model the long-range dependency especially for large instances, and ...
For the surface-aggregated points, SCAN adopts a novel sparse class-agnostic representation of instance centroids, which can not only maintain the sparsity of aligned features to solve the under-segmentation ...
PointGroup (Jiang et al. 2020) proposes a dual-clustering method to refine inaccurate offset predictions around object boundaries. ...
arXiv:2201.05972v1
fatcat:c5mlp7qv4fe3rns5zqpburfbbi
Point Scene Understanding via Disentangled Instance Mesh Reconstruction
[article]
2022
arXiv
pre-print
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Semantic scene reconstruction from point cloud is an essential and challenging task for 3D scene understanding. ...
To circumvent the hurdle, we propose a Disentangled Instance Mesh Reconstruction (DIMR) framework for effective point scene understanding. ...
For example, PointGroup [37] uses sparse 3D CNNs [23, 69, 45, 59] to extract point cloud features, and propose a dual-set clustering algorithm to better distinguish the void space between object instances ...
arXiv:2203.16832v2
fatcat:umhqlzcierh6pclnaf77avvqm4
Language-Grounded Indoor 3D Semantic Segmentation in the Wild
[article]
2022
arXiv
pre-print
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However, current 3D semantic segmentation benchmarks contain only a small number of categories -- less than 30 for ScanNet and SemanticKITTI, for instance, which are not enough to reflect the diversity ...
Extensive experiments show that our approach consistently outperforms state-of-the-art 3D pre-training for 3D semantic segmentation on our proposed benchmark (+9% relative mIoU), including limited-data ...
Acknowledgements This project is funded by the Bavarian State Ministry of Science and the Arts and coordinated by the Bavarian Research Institute for Digital Transformation (bidt). ...
arXiv:2204.07761v2
fatcat:axlrozwpnjejnaarmeqrxyvgla
Deep Learning for 3D Point Clouds: A Survey
[article]
2020
arXiv
pre-print
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It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. ...
To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. ...
A dual-set clustering algorithm and the ScoreNet is further utilized to achieve better grouping results. ...
arXiv:1912.12033v2
fatcat:qiiyvvuulfccxaiihf2mu23k34
PnP-3D: A Plug-and-Play for 3D Point Clouds
[article]
2021
arXiv
pre-print
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With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis. ...
However, there is great potential for development of these networks since the given information of point cloud data has not been fully exploited. ...
Jia, “Pointgroup:
Learning spatial contextual features for large-scale point cloud
Dual-set point grouping for 3d instance ...
arXiv:2108.07378v2
fatcat:atpotz75ujg3rbcgm3qhkd72ay
VoteHMR: Occlusion-Aware Voting Network for Robust 3D Human Mesh Recovery from Partial Point Clouds
[article]
2021
arXiv
pre-print
Preserved Fulltext
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The file type is application/pdf.
3D human mesh recovery from point clouds is essential for various tasks, including AR/VR and human behavior understanding. ...
for statistical 3D human models, such as SMPL. ...
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation. In
Rodgers, and James Davis. 2005. ...
arXiv:2110.08729v1
fatcat:3b25xw26qraalbtuuejmiqedwy
Fooling LiDAR Perception via Adversarial Trajectory Perturbation
[article]
2021
arXiv
pre-print
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flag for the community. ...
When autonomous vehicles are sending LiDAR point clouds to deep networks for perception and planning, could the motion compensation consequently become a wide-open backdoor in those networks, due to both ...
gratefully acknowledge the useful comments and suggestions from Yong Xiao, Wenxiao Wang, Chenzhuang Du, Wang Zhao, Ziyuan Huang, Hang Zhao and Siheng Chen, and also thank Yan Wang, Shaoshuai Shi and Peiyun Wu for ...
arXiv:2103.15326v2
fatcat:sl5oygbamjfvnknb4hqmqr4bca
Hyperbolic band theory
2021
Science Advances
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Tanaka and thank the anonymous reviewers for very useful remarks and suggestions. ...
S.R. was supported by NSERC Discovery Grant #RGPIN-2017-04520, the Canada Foundation for Innovation John R. ...
For the specific case of the Bolza curve, we are able to examine the point-group action directly. ...
doi:10.1126/sciadv.abe9170
pmid:34516893
pmcid:PMC8442860
fatcat:2i3reu4drjhyrjyusxoqxvk6oq
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