Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds
Jingyu Gong,Jiachen Xu,Xin Tan,Jie Zhou,Yanyun Qu,Yuan Xie,Lizhuang Ma +6 more
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TL;DR: This paper proposes a boundary-aware geometric encoding method for 3D point cloud segmentation, incorporating a boundary prediction module and geometric convolution operation to improve feature extraction and aggregation, achieving state-of-the-art performance on ScanNet v2 and S3DIS benchmarks.
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Abstract: Boundary information plays a significant role in 2D image segmentation, while usually being ignored in 3D point cloud segmentation where ambiguous features might be generated in feature extraction, leading to misclassification in the transition area between two objects. In this paper, firstly, we propose a Boundary Prediction Module (BPM) to predict boundary points. Based on the predicted boundary, a boundary-aware Geometric Encoding Module (GEM) is designed to encode geometric information and aggregate features with discrimination in a neighborhood, so that the local features belonging to different categories will not be polluted by each other. To provide extra geometric information for boundary-aware GEM, we also propose a light-weight Geometric Convolution Operation (GCO), making the extracted features more distinguishing. Built upon the boundary-aware GEM, we build our network and test it on benchmarks like ScanNet v2, S3DIS. Results show our methods can significantly improve the baseline and achieve state-of-the-art performance.
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Citations
An object detection algorithm combining semantic and geometric information of the 3D point cloud
TL;DR: Zhang et al. as mentioned in this paper proposed two modules for enhancing useful feature extraction in the SA layer to improve 3D object detection accuracy, focusing on the foreground and boundary scores of the points and reweighing the Furthest Point Sampling (FPS) using the evaluated scores.
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GRAB-Net: Graph-based Boundary-aware Network for Medical Point Cloud Segmentation
01 Jan 2023
TL;DR: Zhang et al. as discussed by the authors proposed a graph-based boundary-aware network (GRAB-Net) with three paradigms, Graph-based Boundary-perception Module (GBM), Outer-boundary Context-assignment Module (OCM), and Inner-Boundary Feature Rectification Module (IFM), for medical point cloud segmentation.
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Neighborhood Co-Occurrence Modeling in 3D Point Cloud Segmentation
Gong, Jingyu,Ye, Zhou,Ma, Lizhuang +2 more
TL;DR: This paper proposes Neighborhood Co-Occurrence Matrix (NCM) to model local co-occurrence relationships in point cloud segmentation, improving performance by 3-5% on three datasets, including Semantic3D, S3DIS, and ScanNet v2.
Fat: Field-Aware Transformer for 3D Point Cloud Semantic Segmentation
Junjie Zhou,Yongping Xiong,C. Chiu,Fangyu Liu,Xiangyang Gong +4 more
- 08 Oct 2023
TL;DR: This paper proposes the Field-Aware Transformer (FAT) that adjusts the attentive receptive fields for objects of different sizes and achieves field-aware learning via two steps: introduce multi-granularity features to each attention layer and allow each point to choose its attentive fields adaptively.
References
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
R. Qi Charles,Hao Su,Mo Kaichun,Leonidas J. Guibas +3 more
- 21 Jul 2017
TL;DR: This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.
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TL;DR: Fully convolutional networks (FCN) as mentioned in this paper were proposed to combine semantic information from a deep, coarse layer with appearance information from shallow, fine layer to produce accurate and detailed segmentations.
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ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
Angela Dai,Angel X. Chang,Manolis Savva,Maciej Halber,Thomas Funkhouser,Matthias NieBner +5 more
- 21 Jul 2017
TL;DR: This work introduces ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations, and shows that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks.
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- 01 Sep 2015
TL;DR: VoxNet is proposed, an architecture to tackle the problem of robust object recognition by integrating a volumetric Occupancy Grid representation with a supervised 3D Convolutional Neural Network (3D CNN).