三維點云語義分割：現狀與挑戰

王藝嫻; 胡雨凡; 孔慶群; 曾慧; 張利欣; 樊彬

doi:10.13374/j.issn2095-9389.2022.12.17.004

三維點云語義分割：現狀與挑戰

doi: 10.13374/j.issn2095-9389.2022.12.17.004

王藝嫻¹,
胡雨凡¹,
孔慶群^{2, 3},
曾慧¹,
張利欣¹,
樊彬^1, ,

1.
北京科技大學智能科學與技術學院，北京 100083
2.
中國科學院自動化研究所，北京 100190
3.
中國科學院大學，北京 100049

基金項目: 北京市自然科學基金資助項目（4202073）；國家自然科學基金資助項目（62076026, 61973029）

詳細信息

通訊作者:
E-mail: bin.fan@ieee.org

中圖分類號: TP391;TP183
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- 文章訪問數: 522
- HTML全文瀏覽量: 75
- PDF下載量: 87
- 被引次數: 0
出版歷程
- 收稿日期: 2022-12-17
- 網絡出版日期: 2023-02-27
- 刊出日期: 2023-10-25

3D point cloud semantic segmentation: state of the art and challenges

WANG Yixian¹,
HU Yufan¹,
KONG Qingqun^{2, 3},
ZENG Hui¹,
ZHANG Lixin¹,
FAN Bin^{1
, ,}

1.
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
2.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China

More Information

Corresponding author: E-mail: bin.fan@ieee.org

摘要

摘要: 隨著獲取點云數據成本下降以及GPU算力的提高，眾多三維視覺場景如自動駕駛、工業控制、MR/XR對三維語義分割的需求日益旺盛，這進一步推動了深度學習模型在三維點云語義分割任務中的發展。近期，深度學習模型在網絡架構上持續創新，如RandLA-Net 和Point Transformer，并突破性地以更低的計算成本提高了分割準確率，但已有的三維點云語義分割綜述介紹的研究工作包含大量早期以及被舍棄的方法，沒有系統地整理這些新型高效的方法，不能很好地體現研究現狀。此外，這部分綜述以輸入網絡的不同數據類型分類各點云語義分割方法，不能有效地體現各方法的演進關系，也不利于對比不同方法的分割性能。針對以上問題，本文面向近3年的研究成果和最新的研究進展，重點歸納了三維點云語義分割中基于不同網絡架構的方法、面臨的挑戰及潛在研究方向，并從3個層面對三維點云語義分割進行了系統地綜述。通過本文，讀者可以較系統地了解三維點云語義分割的數據獲取方式、常見數據集及模型的評價指標，對比基于不同網絡架構的三維點云語義分割方法的發展過程、分割性能和優缺點，并進一步認識三維點云語義分割現存的挑戰和潛在的研究方向。
- 三維視覺 /
- 點云 /
- 語義分割 /
- 深度學習 /
- 網絡架構
Abstract: Decrease in the cost of acquiring 3D point cloud data coupled with the rapid advancements in GPU computing power have resulted in an increased demand for 3D point cloud semantic segmentation in numerous 3D visual applications, including but not limited to autonomous driving, industrial control, and MR/XR, which further advances the development of deep learning methods in 3D point cloud semantic segmentation. Recently, many novel deep learning network architectures, such as RandLA-Net and Point Transformer, have been proposed and have achieved notable improvements in semantic segmentation accuracy while decreasing the computational load. However, previous research on 3D point cloud semantic segmentation methods has focused primarily on relatively early works, whose approaches have been gradually abandoned over the years and cannot accurately reflect the current research status. Moreover, the existing methods have been categorized based on their input data types, making it difficult to compare the segmentation performance of different techniques and not providing a comprehensive view of the relationship between methods using different network architectures. Therefore, this paper reviews the mainstream 3D semantic segmentation methods developed in the last three years using different deep learning network architectures and is organized into three levels. First, the two principal 3D point cloud data acquisition methods, including their customary datasets and metrics to evaluate model performance, are introduced. Second, a systematic review of 3D semantic segmentation methods based on different network architectures is organized, followed by a statistical analysis of the evaluation of performance between different models on two 3D segmentation datasets—S3DIS and ScanNet. The analysis of model performance on these two commonly used datasets includes model structure relevance, strengths, and limitations. Finally, an insightful discussion of the remaining methodological and application challenges and potential research directions is provided. This paper offers an extensive overview of the recent three-year research progress in 3D point cloud semantic segmentation and summarizes various network architecture pipelines, elucidates their fundamental operations, compares the model performance across multiple architectures, discusses their notable strengths and limitations, most importantly, concludes the current challenges and promising research directions for future investigations. Furthermore, this paper enables researchers to effortlessly identify the relevant research and research hotspots among different 3D point cloud semantic segmentation methods based on the analyses presented and aims to update the reviews on 3D point cloud semantic segmentation methods with a better viewpoint and highlight key properties and contributions of proposed methods, providing promising research directions for the main challenges.
- 3D vision /
- point cloud /
- semantic segmentation /
- deep learning /
- network framework

HTML全文

圖 1 二維圖像中的像素(左)與三維點云中的點(右)

Figure 1. Pixels in a 2D image (left) and points in a 3D point cloud (right)

下載: 全尺寸圖片幻燈片

圖 2 三維點云語義分割方法發展里程

Figure 2. Significant milestones in 3D point cloud semantic segmentation methods

下載: 全尺寸圖片幻燈片

圖 3 縮放點積注意力和單層Transformer 的結構(其中注意力模塊的輸入分為查詢Q, 鍵K和值 V, 并得到帶權重的輸出. 最右側虛線框內為多頭自注意力的結構). (a) 縮放點積注意力; (b) 單層Transformer

Figure 3. Structures of scaled dot-product attention and single-layer Transformer encoder (the attention module has three inputs: a query vector Q, key vector K, value vector V, and weighted output. The structure of multi-head self-attention is shown inside the right-most dashed box): (a) scaled dot-product attention; (b) single-layer Transformer encoder

下載: 全尺寸圖片幻燈片

圖 4 不同網絡架構的基本過程示例（其中MSA為多頭自注意力, Add為殘差連接, Norm為正則化, FFN為前饋網絡）. (a) 卷積網絡; (b) 圖卷積網絡; (c) 注意力圖網絡; (d) Transformer

Figure 4. Illustrations of fundamental operations for different network structures (Note: MSA, Add, Norm, and FFN denote multi-head self-attention, residual connection, normalization, and feed-forward networks, respectively): (a) convolution network; (b) graph convolution network; (c) attention graph network; (d) transformer

下載: 全尺寸圖片幻燈片

表 1 點云語義分割常用數據集

Table 1. Popular point cloud semantic segmentation datasets

Dataset name	Dataset type	Sensors	Scene type	# scenes	# classes	Year
S3DIS^[2]	LiDAR point clouds	Matterport camera	indoor	272	13	2016
Semantic3D^[14]	LiDAR point clouds	Terrestrial laser scanners	outdoor	30	8	2017
SemanticKITTI^[15]	LiDAR point clouds	Mobile laser scanners	outdoor	43552	28	2019
ScanNet^[17]	RGB-D images	RGB-D camera	indoor	1513	21	2018
Note: “#” represents “the number of”.

下載: 導出CSV

表 2 不同網絡架構的點云語義分割方法在S3DIS和ScanNet上的評估性能對比

Table 2. Performance evaluation of different semantic segmentation architecture methods on the S3DIS and ScanNet datasets

Method	Input	Architecture	S3DIS batch size	S3DIS number of batch points	S3DIS 6-fold mIoU/%	S3DIS tested on Area5 mIoU/%	ScanNet test set overall accuracy/%	ScanNet test set mIoU/%
Pointnet^[58]	points	MLP			47.60	41.1	73.9	14.69
Pointnet++^[64]	points	MLP			54.50	51.5	84.5	38.28
MinkowskiNet^[20]	voxels	CNN				65.35		72.1
PointConv^[3]	points					50.34		55.6
PointWeb^[23]	points		16		66.70	60.28	85.9
A-CNN^[24]	points						85.4
KPConv^[27]	points				70.60	67.1		68.4
PAConv^[40]	points			4096		66.58
HDGCN^[18]	points	GNN			66.85	59.33
TGNet^[19]	points		16			58.70	66.2
DGCNN^[4]	points		12	4096	56.10
ResGCN-28^[26]	points					60.00
SegGCN^[32]	points		8	8192		63.60		58.9
SPH3D-GCN^[35]	points		16	8192	68.90	59.5		61
GACNet^[21]	points	Attention	16			62.85
RandLA-Net^[31]	points				70.00
AGCN^[34]	points			4096	56.63
PAN^[37]	points		32		66.30		86.7	42.1
PATs^[22]	points	Pure Transformer				60.1
PCT^[42]	points					61.33
PVT^[43]	voxels+ points			4096		68.21
Point Transformer^[5]	points		8		73.50	70.4
LFT-Net^[46]	points					65.2
Fast Point Transformer^[47]	voxels					70.1		72.1
Point Transformer V2^[56]	points					71.6		75.2
Stratified Transformer^[48]	points	Hybrid Transformer	16			72
MinkNet18+Segment-Fusion^[67]	points	Hybrid Transformer				65.3

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