Framework for automated registration of UAV and UGV point clouds using local features in images

2019 ◽  
Vol 98 ◽  
pp. 175-182 ◽  
Author(s):  
Jisoo Park ◽  
Pileun Kim ◽  
Yong K. Cho ◽  
Junsuk Kang
Keyword(s):  
Point Clouds ◽  
Local Features ◽  
Automated Registration

scholarly journals Recognizing Objects in 3D Point Clouds with Multi-Scale Local Features

Sensors ◽  
2014 ◽  
Vol 14 (12) ◽  
pp. 24156-24173 ◽  
Author(s):  
Min Lu ◽  
Yulan Guo ◽  
Jun Zhang ◽  
Yanxin Ma ◽  
Yinjie Lei
Keyword(s):  
Point Clouds ◽  
Local Features ◽  
Multi Scale ◽  
3D Point Clouds

scholarly journals SceneEncoder: Scene-Aware Semantic Segmentation of Point Clouds with A Learnable Scene Descriptor

2020 ◽  
Author(s):  
Jiachen Xu ◽  
Jingyu Gong ◽  
Jie Zhou ◽  
Xin Tan ◽  
Yuan Xie ◽  
...  
Keyword(s):  
Essential Role ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Local Features ◽  
Local Region ◽  
Global Information ◽  
Benchmark Datasets ◽  
Distinguishing Features ◽  
Point Level

Besides local features, global information plays an essential role in semantic segmentation, while recent works usually fail to explicitly extract the meaningful global information and make full use of it. In this paper, we propose a SceneEncoder module to impose a scene-aware guidance to enhance the effect of global information. The module predicts a scene descriptor, which learns to represent the categories of objects existing in the scene and directly guides the point-level semantic segmentation through filtering out categories not belonging to this scene. Additionally, to alleviate segmentation noise in local region, we design a region similarity loss to propagate distinguishing features to their own neighboring points with the same label, leading to the enhancement of the distinguishing ability of point-wise features. We integrate our methods into several prevailing networks and conduct extensive experiments on benchmark datasets ScanNet and ShapeNet. Results show that our methods greatly improve the performance of baselines and achieve state-of-the-art performance.

Pairwise registration of TLS point clouds by deep multi-scale local features

2020 ◽  
Vol 386 ◽  
pp. 232-243
Author(s):  
Wei Li ◽  
Cheng Wang ◽  
Chenglu Wen ◽  
Zheng Zhang ◽  
Congren Lin ◽  
...  
Keyword(s):  
Point Clouds ◽  
Local Features ◽  
Multi Scale ◽  
Pairwise Registration

scholarly journals INDOOR 3D POINT CLOUDS SEMANTIC SEGMENTATION BASES ON MODIFIED POINTNET NETWORK

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 369-373
Author(s):  
J. Zhao ◽  
X. Zhang ◽  
Y. Wang
Keyword(s):  
Point Cloud ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Local Features ◽  
Indoor Navigation ◽  
Convolution Kernel ◽  
Structural Elements ◽  
Global Features ◽  
3D Point Clouds ◽  
Point Cloud Segmentation

Abstract. Indoor 3D point clouds semantics segmentation is one of the key technologies of constructing 3D indoor models,which play an important role on domains like indoor navigation and positioning,intelligent city, intelligent robot etc. The deep-learning-based methods for point cloud segmentation take on higher degree of automation and intelligence. PointNet,the first deep neural network which manipulate point cloud directly, mainly extracts the global features but lacks of learning and extracting local features,which causes the poor ability of segmenting the local details of architecture and affects the precision of structural elements segmentation . Focusing on the problems above,this paper put forward an automatic end-to-end segmentation method base on the modified PointNet. According to the characteristic that the intensity of different indoor structural elements differ a lot, we input the point cloud information of 3D coordinate, color and intensity into the feature space of points. Also,a MaxPooling is added into the original PointNet network to improve the ability of attracting and learning local features. In addition, replace the 1×1 convolution kernel of original PointNet with 3×3 convolution kernel in the process of attracting features to improve the segmentation precision of indoor point cloud. The result shows that this method improves the automation and precision of indoor point cloud segmentation for the precision achieves over 80% to segment the structural elements like wall,door and so on ,and the average segmentation precision of every structural elements achieves 66%.

scholarly journals Effective Point Cloud Analysis Using Multi-Scale Features

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5574
Author(s):  
Qiang Zheng ◽  
Jian Sun
Keyword(s):  
Spatial Distribution ◽  
Spatial Relationship ◽  
Point Clouds ◽  
Local Features ◽  
Multi Scale ◽  
Proposed Model ◽  
Local Patterns ◽  
Previous State ◽  
Lightweight Structure ◽  
Independent Features

Fully exploring the correlation of local features and their spatial distribution in point clouds is essential for feature modeling. This paper, inspired by convolutional neural networks (CNNs), explores the relationship between local patterns and point coordinates from a novel perspective and proposes a lightweight structure based on multi-scale features and a two-step fusion strategy. Specifically, local features of multi-scales and their spatial distribution can be regarded as independent features corresponding to different levels of geometric significance, which are extracted by multiple parallel branches and then merged on multiple levels. In this way, the proposed model generates a shape-level representation that contains rich local characteristics and the spatial relationship between them. Moreover, with the shared multi-layer perceptrons (MLPs) as basic operators, the proposed structure is so concise that it converges rapidly, and so we introduce the snapshot ensemble to improve performance further. The model is evaluated on classification and part segmentation tasks. The experiments prove that our model achieves on-par or better performance than previous state-of-the-art (SOTA) methods.

scholarly journals Fast registration of laser scans with 4-point congruent sets - what works and what doesn't

2014 ◽  
Vol II-3 ◽  
pp. 149-156 ◽  
Author(s):  
P. W. Theiler ◽  
J. D. Wegner ◽  
K. Schindler
Keyword(s):  
Laser Scanning ◽  
Error Function ◽  
Near Field ◽  
Point Clouds ◽  
What Works ◽  
Scan Registration ◽  
Field Bias ◽  
Indoor Scenarios ◽  
Automated Registration ◽  
Repeated Evaluation

Sampling-based algorithms in the mould of RANSAC have emerged as one of the most successful methods for the fully automated registration of point clouds acquired by terrestrial laser scanning (TLS). Sampling methods in conjunction with 3D keypoint extraction, have shown promising results, e.g. the recent K-4PCS (Theiler et al., 2013). However, they still exhibit certain improbable failures, and are computationally expensive and slow if the overlap between scans is low. Here, we examine several variations of the basic K-4PCS framework that have the potential to improve its runtime and robustness. Since the method is inherently parallelizable, straight-forward multi-threading already brings down runtimes to a practically acceptable level (seconds to minutes). At a conceptual level, replacing the RANSAC error function with the more principled MSAC function (Torr and Zisserman, 2000) and introducing a minimum-distance prior to counter the near-field bias reduce failure rates by a factor of up to 4. On the other hand, replacing the repeated evaluation of the RANSAC error function with a voting scheme over the transformation parameters proved not to be generally applicable for the scan registration problem. All these possible extensions are tested experimentally on multiple challenging outdoor and indoor scenarios.

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