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TNIE-SLAM: Neural Implicit Surface Reconstruction for Tracking-Oriented SLAM

Journal of Intelligent & Robotic Systems 2025年第2期111卷 1-22页

作者： Gan, Baolin Zhang, Congxuan Chen, Shuaixin Chen, Zhen He, Chao Lu, Ke Lu, Feng School of Instrument Science and Optoelectronic Engineering Nanchang Hangkong University Nanchang China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Information Engineering Nanchang Hangkong University Nanchang China The College of Engineering Science University of Chinese Academy of Sciences Beijing China

Recent studies on simultaneous localization and mapping (SLAM) have tended to employ implicit neural representation, which can improve the efficiency and robustness of SLAM system. However, these methodologies still face challenges, such as tracking failures and low-precision mapping. In this paper, we propose a dense reconstruction visual SLAM system enhanced with closed-loop threading and local map optimization, named TNIE-SLAM. First, we propose a tracking module that utilizes the similarity of ORB feature descriptors and the feature overlap rate of the current frame to model key frames, and then we define a complete and accurate initial map based on full bundle adjustment, which addresses the issue of tracking failure due to undermapped areas. Second, we add the 2D features of the initial map to the spatiotemporal encoding module to obtain the 3D features, enabling real-time prediction and tracking of unknown areas. Finally, considering the low-precision mapping issue arising from the complex geometric shapes of objects within the scene, we propose a local map optimization module that utilizes truncated signed distance fields to model 3D features and update the spatial occupancy of boundary and contour features of objects. We test our method on the synthetic Replica dataset and the real-world ScanNet and TUM RGB-D datasets to compare with some state-of-the-art RGB-D SLAM methods, and the experimental results indicate our method performs well in both tracking and mapping accuracy, surpassing the existing dense neural RGB-D SLAM methods.

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Combining local binary patterns for scene recognition

Journal of Software

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Journal of Software 2014年第1期9卷 203-210页

作者： Song, Minguang Guo, Ping Image Processing Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. An improved representation called Local Difference Binary pattern (LDBP) also was proposed and performed better than that of PACT. LDBP is based on the comparisons between center pixel and its neighboring pixels, but the relationship among neighbor pixels is not considered. In this paper, we propose to combine Local Neighbor Binary pattern (LNBP) with LDBP to construct a spatial representation for scene recognition, because that LNBP can provide complementary information regarding neighboring pixels for LDBP. Experiments on widely used datasets demonstrate that the performance of image recognition is further improved with proposed method. © 2014 ACADEMY PUBLISHER.

关键词： Pixels

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Improving depth resolution of diffuse optical tomography with an exponential adjustment method based on maximum singular value of layered sensitivity

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Chinese Optics Letters 2008年第12期6卷 886-888页

作者：牛海晶郭平宋晓东蒋田仔 Image Processing and Pattern Recognition Laboratory Beijing Normal University School of Computer Science and Technology Beijing Institute of Technology School of Aerospace Science and Engineering Beijing Institute of Technology National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

The sensitivity of diffuse optical tomography （DOT） imaging exponentially decreases with the increase of photon penetration depth, which leads to a poor depth resolution for DOT. In this letter, an exponential adjustment method （EAM） based on maximum singular value of layered sensitivity is proposed. Optimal depth resolution can be achieved by compensating the reduced sensitivity in the deep medium. Simulations are performed using a semi-infinite model and the simulation results show that the EAM method can substantially improve the depth resolution of deeply embedded objects in the medium. Consequently, the image quality and the reconstruction accuracy for these objects have been largely improved.

关键词： Diagnostic radiography Electroabsorption modulators image quality Light absorption Medical imaging Optical tomography

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image segmentation based on visual perception model

Image segmentation based on visual perception model

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2012 2nd International Conference on Computer Application and System Modeling, ICCASM 2012

作者： Shao, Shuai Duan, Fuqing Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9789491216008

image segmentation is the basis of image processing and image analysis. However, there are no common method that can be used in natural images, and present methods fail to explain understandings of human's visual system. In this paper, we propose to apply Karklin's visual perception model to extract feature vectors of images, and the features are clustered with K-means method. The results obtained in feature space are projected back to the image space to finish segmentation. A comparison with the normalized Cuts (Ncut) method is done, and it turns out that proposed method outperform Ncut in texture rich images. © the authors.

关键词： image segmentation

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Software fault prediction framework based on aiNet algorithm

Software fault prediction framework based on aiNet algorithm

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2011 7th International Conference on Computational Intelligence and Security, CIS 2011

作者： Yin, Qian Luo, Ruiyi Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9780769545844

Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high fault prone metrical data are much scattered and multi-centers can represent the whole dataset better, we used artificial immune network (aiNet) algorithm to extract and simplify data from the modules that have been tested, then generated multi-centers for each network by Hierarchical Clustering. The proposed framework acquires information along with the testing process timely and adjusts the network generated by aiNet algorithm dynamically. Experimental results show that higher accuracy can be obtained by using the proposed framework. © 2011 IEEE.

关键词： Forecasting

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Empirically validating software metrics for risk prediction based on intelligent methods

Journal of Digital Information Management

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Journal of Digital Information Management 2007年第3期5卷 99-106页

作者： Xu, Zhihong Zheng, Xin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China School of Computer Science Beijing Institute of Technology Beijing 100081 China

The software systems which are related to national science and technology projects are very crucial. This kind of systems always involves high technical factors and has to spend a large amount of money, so the quality and reliability of the software deserve to be further studied. Hence, we propose to apply four intelligent classification techniques most used in data mining fields, including Bayesian belief networks (BBN), nearest neighbor (NN), rough set (RS) and decision tree (DT), to validate the usefulness of software metrics for risk prediction. Results show that comparing with metrics such as Lines of code (LOC) and Cyclomatic complexity (V(G)) which are traditionally used for risk prediction, Halstead program difficulty (D), Number of executable statements (EXEC) and Halstead program volume (V) are the more effective metrics as risk predictors. By analyzing obtained results we also found that BBN was more effective than the other three methods in risk prediction.

关键词： Bayesian networks

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Combining the contrast information with WLD for texture classification

Combining the contrast information with WLD for texture clas...

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2012 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2012

作者： Dawood, Hussain Dawood, Hassan Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9781467300865

A considerable amount of research work has been done for texture classification using local or global feature extraction methods. Inspired by Weber's Law, a simple and robust Weber Local Descriptor (WLD) is a recently developed for local feature extraction. This WLD method did not consider the contrast information. In order to improve texture classification accuracy, we propose a hybrid approach that combines the WLD with contrast information in this paper. It utilizes the histogram of two complementary features WLD and the image variance calculated with the Probability Weighted Moments. Support vector machine is used for classification. The comparison of the proposed method with state of art methods like local binary pattern and WLD is experimental investigated on two publically available dataset, named as Brodatz and KTH-TIPS2-a. Results show that our proposed method outperforms over the state of art methods for texture classification. © 2012 IEEE.

关键词： Support vector machines

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Isomap and neural networks based image registration scheme

引用

3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Xu, Anbang Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)3540344373

A novel image registration scheme is proposed. In the proposed scheme, the complete isometric mapping (Isomap) is used to extract features from the image sets, and these features are input vectors of feedforward neural networks. Neural network outputs are those translation, rotation and scaling parameters with respect to reference and observed image sets. Comparative experiments for Isomap based method, the discrete cosine transform (DCT) and Zernike moment are performed. The results show that the proposed scheme is not only accurate but also remarkably robust to noise. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Self organizing maps

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Global matching to enhance the strength of local intensity order pattern feature descriptor

Global matching to enhance the strength of local intensity o...

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10th International Symposium on Neural Networks, ISNN 2013

作者： Dawood, Hassan Dawood, Hussain Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9783642390647

Local intensity order pattern feature descriptor is proposed to extract the feature of image recently. However, it did not provide the global information of an image. In this paper, a simple, efficient and robust feature descriptor is presented, which is realized by adding the global information to local intensity features. A descriptor, which utilizes local intensity order pattern and/or global matching, is proposed to gather the global information with local intensity order. Experimental results shows that the proposed hybrid approach outperform over the state-of-the art feature extraction method like scale-invariant feature transform, local intensity order pattern and DAISY for standard oxford dataset. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Feature extraction

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Rendering scenes with participating media based on RBFs for photon mapping using graphics hardware

Rendering scenes with participating media based on RBFs for ...

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12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry, VRCAI 2013

作者： Wang, Yuanlong Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9781450325905

The photon mapping is one of the more widely used algorithms for rendering scenes with participating media. Currently, it suffers from two main problems: one is how to improve the rendering efficiency, and another is how to reduce large memory requirement to store the photons. In this paper, we propose a hardware-accelerated algorithm that is based on radial basis function model for rendering participating media with multiple scattering. The radial basis function model is used to describe the photon distribution in participating media, and the main purpose is to provide high reconstruction quality using a relatively low number of basis function terms. Consequently, it can result in faster rendering. The radiance estimation is designed according to the radial basis function model. The implementation demonstrates that proposed method is the attractive low memory representation and it is capable of fully simulating the scattering of participating media with interactive feedback to the user. © 2013 ACM.

关键词： Radial basis function networks

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