scholarly journals An iterative refinement method of point cloud for binocular vision 3D reconstruction

2021 ◽  
Vol 2078 (1) ◽  
pp. 012038
Author(s):  
Junwei Hu ◽  
Jifeng Sun ◽  
Yinggang Li ◽  
Qi Zhang ◽  
Shuai Zhao ◽  
...  
Keyword(s):  
Point Cloud ◽  
Depth Map ◽  
Input Image ◽  
Iterative Refinement ◽  
Data Set ◽  
Cloud Processing ◽  
Refinement Method ◽  
Binocular Stereo ◽  
The Difference ◽  
Deep Learning Network

Abstract This paper introduces a new binocular stereo deep learning network based on point cloud, which can realize higher precision point cloud reconstruction through continuous iteration of the network. Our method directly carries out point cloud processing on the target, calculates the difference between the current depth map and the real depth, estimates the loss according to the predicted point cloud and the information of the dual view input image, and then uses the appropriate loss function to iteratively process the point cloud. In addition, we can customize the number of iterations to achieve higher precision point cloud effect. The proposed network basically achieves good results on KITTI data set.

Design and Optimization of LIDAR Based 3D Point Cloud Parsers and Algorithms for Mobile Robotics Applications

2019 ◽  
Author(s):  
Michael Benvenuto ◽  
Akin Tatoglu
Keyword(s):  
Point Cloud ◽  
Mobile Robotics ◽  
Autonomous Robots ◽  
Algorithm Design ◽  
Computational Time ◽  
Multiple Sources ◽  
Data Set ◽  
Design And Optimization ◽  
Urban City ◽  
The Difference

Abstract With mobile autonomous robots on the rise, data structuring and algorithm design plays a significant role in how fast data can be parsed and processed. With robotic systems that are decreasing in size and increasing with complexity, the speed at which data can be processed from multiple sources is crucial to how the system as a whole works. This paper plans to show the difference between certain computational algorithm complexities, both time and space complexity, in order to demonstrate the key ideas in data parsing for systems where computational time of an algorithm can affect the outcome performance of a robot dramatically. The algorithmic types being analyzed in this paper are in relation to a 3D LIDAR scanner in order to produce a point cloud as the output from pre-recorded files. Trigonometric calculations will need to be done in order to produce this output and each file used will be verified using a program supplied from the manufacturer of the LIDAR, Velodyne, in order to ensure the data is being read correctly. The sample data consists of two specific recorded sets, a loading bay and a downtown urban city. Each of data set covers the two configurable outputs, a 20 Mhz Dual Return mode and a 10 Mhz Single Return mode, providing a reasonable range in terms of size in bytes. This paper will show various levels of optimization in areas of trigonometric functions, specifically sine and cosine, algorithmic design, memory safety and defragmenting, and pointer manipulation in order to produce a robust, complex yet ideal algorithm for loading large sets of data rapidly and quickly while holding the level of reliability high. All software written in this paper intends to be natively implemented, meaning no operating system specific external binaries will be used for the end product.

Difference Fourier Analysis of Glucose Embedded and Frozen Hydrated Purple Membrane

1982 ◽  
Vol 40 ◽  
pp. 74-75
Author(s):  
Jules S. Jaffe ◽  
Robert M. Glaeser
Keyword(s):  
Purple Membrane ◽  
Data Set ◽  
High Resolution Data ◽  
X Ray ◽  
X Ray Crystallography ◽  
Fourier Techniques ◽  
Versus Protein ◽  
The Difference ◽  
Difference Fourier ◽  
Ideal Method

Although difference Fourier techniques are standard in X-ray crystallography it has only been very recently that electron crystallographers have been able to take advantage of this method. We have combined a high resolution data set for frozen glucose embedded Purple Membrane (PM) with a data set collected from PM prepared in the frozen hydrated state in order to visualize any differences in structure due to the different methods of preparation. The increased contrast between protein-ice versus protein-glucose may prove to be an advantage of the frozen hydrated technique for visualizing those parts of bacteriorhodopsin that are embedded in glucose. In addition, surface groups of the protein may be disordered in glucose and ordered in the frozen state. The sensitivity of the difference Fourier technique to small changes in structure provides an ideal method for testing this hypothesis.

scholarly journals 2D–3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryoya Shiode ◽  
Mototaka Kabashima ◽  
Yuta Hiasa ◽  
Kunihiro Oka ◽  
Tsuyoshi Murase ◽  
...  
Keyword(s):  
Wrist Joint ◽  
High Accuracy ◽  
Training Data ◽  
Small Data ◽  
Data Set ◽  
Accuracy Estimation ◽  
X Ray ◽  
Learning Network ◽  
Forearm Bone ◽  
Deep Learning Network

AbstractThe purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

scholarly journals A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 991
Author(s):  
Yuta Nakahara ◽  
Toshiyasu Matsushima
Keyword(s):  
Computational Cost ◽  
Block Size ◽  
Input Image ◽  
Generative Model ◽  
Image Size ◽  
Variable Block ◽  
General Data ◽  
The Difference ◽  
Segmentation Of Images ◽  
Target Data

In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compression, researchers have mainly focused on the coding procedure that outputs the coded sequence from the input image, and the assumption of the stochastic generative model is implicit. In these studies, there is a difficulty in discussing the difference between the expected code length and the entropy of the stochastic generative model. We solve this difficulty for a class of images, in which they have non-stationarity among segments. In this paper, we propose a novel stochastic generative model of images by redefining the implicit stochastic generative model in a previous coding procedure. Our model is based on the quadtree so that it effectively represents the variable block size segmentation of images. Then, we construct the Bayes code optimal for the proposed stochastic generative model. It requires the summation of all possible quadtrees weighted by their posterior. In general, its computational cost increases exponentially for the image size. However, we introduce an efficient algorithm to calculate it in the polynomial order of the image size without loss of optimality. As a result, the derived algorithm has a better average coding rate than that of JBIG.

scholarly journals PCT: Point cloud transformer

2021 ◽  
Vol 7 (2) ◽  
pp. 187-199
Author(s):  
Meng-Hao Guo ◽  
Jun-Xiong Cai ◽  
Zheng-Ning Liu ◽  
Tai-Jiang Mu ◽  
Ralph R. Martin ◽  
...  
Keyword(s):  
Language Processing ◽  
Point Cloud ◽  
Nearest Neighbor ◽  
Semantic Segmentation ◽  
Nearest Neighbor Search ◽  
Local Context ◽  
Irregular Domain ◽  
Cloud Processing ◽  
Neighbor Search ◽  
Farthest Point

AbstractThe irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.

Event detection of different English data sources based on transfer learning

2021 ◽  
pp. 1-11
Author(s):  
Yanan Huang ◽  
Yuji Miao ◽  
Zhenjing Da
Keyword(s):  
Transfer Learning ◽  
Event Detection ◽  
Visual Analysis ◽  
Learning Algorithm ◽  
Data Sources ◽  
Data Set ◽  
Data Source ◽  
Single Data Source ◽  
The Difference ◽  
Single Data

The methods of multi-modal English event detection under a single data source and isomorphic event detection of different English data sources based on transfer learning still need to be improved. In order to improve the efficiency of English and data source time detection, based on the transfer learning algorithm, this paper proposes multi-modal event detection under a single data source and isomorphic event detection based on transfer learning for different data sources. Moreover, by stacking multiple classification models, this paper makes each feature merge with each other, and conducts confrontation training through the difference between the two classifiers to further make the distribution of different source data similar. In addition, in order to verify the algorithm proposed in this paper, a multi-source English event detection data set is collected through a data collection method. Finally, this paper uses the data set to verify the method proposed in this paper and compare it with the current most mainstream transfer learning methods. Through experimental analysis, convergence analysis, visual analysis and parameter evaluation, the effectiveness of the algorithm proposed in this paper is demonstrated.

scholarly journals An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1985
Author(s):  
Emre Özdemir ◽  
Fabio Remondino ◽  
Alessandro Golkar
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Computational Power ◽  
Specific Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Current State ◽  
Data Source ◽  
Point Cloud Classification ◽  
And Training

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification have been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while others are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art methods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.

Magnetotelluric static shift: Estimation and removal using the cokriging method

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. F25-F34 ◽  
Author(s):  
Benoit Tournerie ◽  
Michel Chouteau ◽  
Denis Marcotte
Keyword(s):  
Apparent Resistivity ◽  
A Priori ◽  
Shift Factor ◽  
Static Shift ◽  
Geostatistical Model ◽  
Data Set ◽  
Resistivity Sounding ◽  
The Difference ◽  
Cross Variogram

We present and test a new method to correct for the static shift affecting magnetotelluric (MT) apparent resistivity sounding curves. We use geostatistical analysis of apparent resistivity and phase data for selected periods. For each period, we first estimate and model the experimental variograms and cross variogram between phase and apparent resistivity. We then use the geostatistical model to estimate, by cokriging, the corrected apparent resistivities using the measured phases and apparent resistivities. The static shift factor is obtained as the difference between the logarithm of the corrected and measured apparent resistivities. We retain as final static shift estimates the ones for the period displaying the best correlation with the estimates at all periods. We present a 3D synthetic case study showing that the static shift is retrieved quite precisely when the static shift factors are uniformly distributed around zero. If the static shift distribution has a nonzero mean, we obtained best results when an apparent resistivity data subset can be identified a priori as unaffected by static shift and cokriging is done using only this subset. The method has been successfully tested on the synthetic COPROD-2S2 2D MT data set and on a 3D-survey data set from Las Cañadas Caldera (Tenerife, Canary Islands) severely affected by static shift.

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