PointGS: Bridging and fusing geometric and semantic space for 3D point cloud analysis

Jiang, Chenru; Huang, Kaizhu; Wu, Junwei; Wang, Xinheng; Xiao, Jimin; Hussain, Amir

doi:10.1016/j.inffus.2022.10.016

Cited by 20 publications

(1 citation statement)

References 53 publications

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“…Based on RandLA-Net architecture, GA-Net [33] introduced attention mechanism through point-dependent to capture long-range dependencies and point-independent to obtain global contextual features. PointGS [34] proposed a dual-space fusion model, which integrated geometric to semantic spaces. Moreover, research introduces geometric relation features that reflect comprehensibly one point in a specific local graph.…”

Section: B Point-based Methodsmentioning

confidence: 99%

Local Graph Point Attention Network in Point Cloud Segmentation

Tran

Lee

et al. 2023

IEEE Access

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Exploiting global factors and embedding them directly into local graphs in point clouds are challenging due to dense points and irregular structure. To accomplish this goal, we propose a novel end-toend trainable graph attention network that extracts global features in terms of local graphs. Our network presents general local graph, which obtains the most fundamental features based on point order positions in different neighborhoods. Central point attention is introduced to share weights with neighboring points to reinforce central point impacts. As a result, one point in the specific local graph can obtain global features from corresponding ones in other neighborhoods but still focus on its area by encoded central point weight sharing. Experimental results on benchmark point cloud segmentation datasets demonstrate our proposed network's competitive performance. INDEX TERMS 3D point cloud, central point, local graph, point attention, segmentation.

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Section: B Point-based Methodsmentioning

confidence: 99%

Local Graph Point Attention Network in Point Cloud Segmentation

Tran

Lee

et al. 2023

IEEE Access

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3-2-3 Multi-AI Segmentation Framework: LoD-Based, Incremental Segmentation of 3D Scan Data Using Any 2D AI

Solheiro,

Kent,

Toyoda

2024

Lecture Notes in Computer Science

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In the age of spatial computing, computer vision is central, and efficient segmentation of 3D scan data becomes a fundamental task. Existing segmentation methods are often locked to specific AI models, lack level-of-detail (LoD) capabilities, and do not support efficient incremental segmentation. These limitations hinder their application to XR systems that integrate architectural and urban scales, which demand both at scale and detailed, up-to-date segmentation information, while leveraging limited local hardware in distributed computing environments.In this work, we present a novel framework that integrates multiple 2D AI through AI-agnostic 3D geometry feature fusion, ensuring spatial consistency while taking advantage of the rapid advancements in 2D AI models. Our framework performs LoD segmentation, enabling swift segmentation of downsampled geometry and full detail on needed segments. Additionally, it progressively builds a segmentation database, processing only newly added data, thereby avoiding point cloud reprocessing, a common limitation in previous methods.In our use case, our framework analyzed a public building based on three scans: a drone LiDAR capture of the exterior, a static LiDAR capture of a room, and a user-held RGB-D camera capture of a section of the room. Our approach provided a fast understanding of building volumes, room elements, and a fully detailed geometry of a requested object, a “panel with good lighting and a view to a nearby building”, to implement an XR activity.Our preliminary results are promising for applications in other urban and architectural contexts and point to further developments in our Geometric Data Inference AI as a cornerstone for deeper, more accurate Multi-AI integration.

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