Accurate and Serialized Dense Point Cloud Reconstruction for Aerial Video Sequences

Traditional multi-view stereo (MVS) is not applicable for the point cloud reconstruction of serialized video frames. Among them, the exhausted feature extraction and matching for all the prepared frames are time-consuming, and the scope of the search requires covering all the key frames. In this pap...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 15; číslo 6; s. 1625
Hlavní autoři: Xu, Shibiao, Pan, Bingbing, Zhang, Jiguang, Zhang, Xiaopeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.03.2023
Témata:
Accuracy
Algorithms
Cameras
covisibility
data collection
Datasets
design
Digital video
Feature extraction
Frames (data processing)
Image processing
Image reconstruction
Machine vision
Matching
Methods
multi-view
Neural networks
Performance enhancement
point cloud
Pose estimation
Remote sensing
Semantics
Sensors
serialized reconstruction
Sparsity
China
ISSN:2072-4292, 2072-4292
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Shrnutí:Traditional multi-view stereo (MVS) is not applicable for the point cloud reconstruction of serialized video frames. Among them, the exhausted feature extraction and matching for all the prepared frames are time-consuming, and the scope of the search requires covering all the key frames. In this paper, we propose a novel serialized reconstruction method to solve the above issues. Specifically, a joint feature descriptors-based covisibility cluster generation strategy is designed to accelerate the feature matching and improve the performance of the pose estimation. Then, a serialized structure-from-motion (SfM) and dense point cloud reconstruction framework is designed to achieve high efficiency and competitive precision reconstruction for serialized frames. To fully demonstrate the superiority of our method, we collect a public aerial sequences dataset with referable ground truth for the dense point cloud reconstruction evaluation. Through a time complexity analysis and the experimental validation in this dataset, the comprehensive performance of our algorithm is better than the other compared outstanding methods.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15061625