LSANet: Feature Learning on Point Sets by Local Spatial Aware Layer

Chen, Lin-Zhuo; Li, Xuanyi; Fan, Deng-Ping; Wang, Kai; Lu, Shao-Ping; Cheng, Ming–Ming

doi:10.48550/arxiv.1905.05442

Cited by 13 publications

(18 citation statements)

References 25 publications

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“…E.g., RandLA-Net [95] achieves an overall IoU of 76.0% on the reduced-8 subset of Semantic3D, but a very low IOU of 41.1% on the class of hardscape. • The majority of existing approaches [5], [27], [52], [170], [171] work on small point clouds (e.g., 1m×1m with 4096 points). In practice, the point clouds acquired by depth sensors are usually immense and large-scale.…”

Section: Discussionmentioning

confidence: 99%

“…This module is less sensitive to outliers and can select a representative subset of points. To better capture the spatial distribution of a point cloud, Chen et al [171] proposed a Local Spatial Aware (LSA) layer to learn spatial awareness weights based on the spatial layouts and the local structures of point clouds. Similar to CRF, Zhao et al [172] proposed an Attention-based Score Refinement (ASR) module to post-process the segmentation results produced by the network.…”

Section: Hybrid Representationmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for 3D Point Clouds: A Survey

Guo

Wang

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

1,447

590

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Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.

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Section: Discussionmentioning

confidence: 99%

Section: Hybrid Representationmentioning

confidence: 99%

Deep Learning for 3D Point Clouds: A Survey

Guo

Wang

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

1,447

590

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show abstract

“…Specifically designed layers are frequently imported for better local feature extraction. LSANet [67] improved feature extraction by introducing a novel Local Spatial Aware (LSA) layer, which takes the spatial distribution of point clouds into account by Spatial Distribution Weights (SDWs). PyramNet [68] introduced two novel operators, Graph Embedding Module (GEM) and Pyramid Attention Network (PAN), resulting in more accurate local feature extraction.…”

Section: B Point-based Methodsmentioning

confidence: 99%

“…PointNet [10] PointNet++ [60] Anand et al [56] Wolf et al [57] Weinmann et al [59] RF_MSSF[58] MS+CU [61] PointSIFT [62] SO-Net [64] RandLA-Net [52] Engelmann et al [63] PAT [66] LSANet [67] PyramNet [68] PointCNN [69] Tangent Convolution [75] DAR-Net [76] A-CNN [70] KPConv [71] ShellNet [77] DPC [72] PointAtrousGraph [74] PointAtrousNet [73] G+RCU [61] RSNet [78] SPLATNet [79] LatticeNet [81] Lawin et al [83] Boulch et al [84] SqueezeSeg [11] SqueezeSegV2[] Gao et al [86] RangeNet++ [87] DeepTemporalSeg [88] LiSeg [89] PointSeg [90] RIU-Net [91] Huang et al [92] SEGCloud [93] FCPN [94] 3DCNN-DQN-RNN[100] SSCN [95] Zhang et al [96] ScanComplete [97] VV-NET [98] VolMap [99] SPG [101] GACNet [102] Jiang et al…”

Section: D Semantic Segmentationmentioning

confidence: 99%

Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study

Gao¹,

Pan²,

Li³

et al. 2020

Preprint

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3D LiDAR semantic segmentation is a pivotal task that is widely involved in many applications, such as autonomous driving and robotics. Studies of 3D LiDAR semantic segmentation have recently achieved considerable development, especially in terms of deep learning strategies. However, these studies usually rely heavily on considerable fine annotated data, while point-wise 3D LiDAR datasets are extremely insufficient and expensive to label. The performance limitation caused by the lack of training data is called the data hungry effect. This survey aims to explore whether and how we are hungry for 3D LiDAR data for semantic segmentation. Thus, we first provide an organized review of existing 3D datasets and 3D semantic segmentation methods. Then, we provide an in-depth analysis of three representative datasets and several experiments to evaluate the data hungry effects in different aspects. Efforts to solve data hungry problems are summarized for both 3D LiDAR-focused methods and general-purpose methods. Finally, insightful topics are discussed for future research on data hungry problems and open questions.

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“…As shown in Table 4, our RandLA-Net achieves on-par or better performance than state-of-the-art methods. Note that, most of these baselines [38,29,64,63,51,6] tend to use sophisticated but expensive operations or samplings to optimize the networks on small blocks (e.g., 1×1 meter) of point clouds, and the relatively small rooms act in their favours to be divided into tiny blocks. By contrast, RandLA-Net takes the entire rooms as input and is able to efficiently infer per-point semantics in a single pass.…”

Section: Semantic Segmentation On Benchmarksmentioning

confidence: 99%

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

Yang

Xie

et al. 2019

Preprint

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We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/postprocessing steps, most existing approaches are only able to be trained and operate over small-scale point clouds. In this paper, we introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Although remarkably computation and memory efficient, random sampling can discard key features by chance. To overcome this, we introduce a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details. Extensive experiments show that our RandLA-Net can process 1 million points in a single pass with up to 200× faster than existing approaches. Moreover, our RandLA-Net clearly surpasses state-of-the-art approaches for semantic segmentation on two large-scale benchmarks Semantic3D and Se-manticKITTI.

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LSANet: Feature Learning on Point Sets by Local Spatial Aware Layer

Cited by 13 publications

References 25 publications

Deep Learning for 3D Point Clouds: A Survey

Deep Learning for 3D Point Clouds: A Survey

Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

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