基于多角度光学投影表面重建的三维自发荧光光源定位算法

在自发荧光断层成像（Bioluminescent tomography imaging,BLT）中,双模态融合（光学模态与结构模态）可充分利用结构模态提供的高精度3D几何结构,重建三维表面荧光光通量分布,进而实现小动物内部荧光光源定位.然而,与纯光学模态相比,双模态融合存在采集系统复杂、成本高、数据处理繁琐及存在电离辐射（如CT）等问题.因此,研究基于纯光学3D几何结构的自发荧光光源定位方法对BLT具有重要意义. 本文在搭建纯光学自发荧光断层系统（All-optical bioluminescence tomography system,AOBTS）的基础上,提出一种基于多角度光学投影表面重建的三维自发荧光光源定位方法. 本方法由基于多角度光学投影的3D表面重建、多角度荧光无缝融合、荧光光通量的量化校正以及自发荧光内部光源重建4部分组成. 通过真实小鼠内部植入荧光光源实验表明,与传统纯光学方法相比,本文提出方法不仅改进了3D表面重建方法,而且增加了多角度荧光无缝融合,可实现真实小鼠的三维自发荧光光源定位,初步实验证明具有小动物预临床实验潜力.     

Two-stage source reconstruction algorithm for bioluminescence tomography using hybrid FEM

A two-stage source reconstruction algorithm for bioluminescence tomography (BLT) is developed using hybrid finite element method (FEM). The proposed algorithm takes full advantages of linear and quadratic FEMs, which can be used to localize and quantify bioluminescent source accurately. In the first stage, a large permissible region is roughly determined and then iteratively evolved to reduce matrix dimension using efficient linear FEM. In the final stage, high-convergence quadratic FEM is applied to improve reconstruction result. Both numerical simulation and physical experiment are performed to evaluate the proposed algorithm. The relevant results demonstrate that quantitative reconstruction can be well achieved in terms of computation efficiency, source position, power density, and total power when compared with previous studies.     

A generic and parallel algorithm for 2D digital curve polygonal approximation

In this paper, we present a generic topological and geometrical framework which allows to define and control several parallel algorithms for 2D digital curve approximation. The proposed technique is based on combinatorial map simplifications guided by geometrical criteria. We illustrate the genericity of the framework by defining three contour simplification methods: a polygonal approximation one based an area deviation computation; a digital straight segments reconstruction one which guaranties to obtain a loss-less representation; and a moment preserving simplification one which simplifies the contours while preserving geometrical moments of the image regions. Thanks to a complete experimental evaluation, we demonstrate that the proposed methods can be efficiently implemented in a multi-thread environment to simplify labeled image contours.     

A new unsupervised cube-based algorithm for iso-surface generation

This paper introduces a new algorithm which automatically produces polygonal representations of 3D structures within a volume data set built from a stack of parallel cross-sections. Several methods of 3D surface reconstruction have already been proposed ranging from heuristic approaches for constructing 3D surfaces from 2D contours to the Marching Cubes (MC) approach where the different configurations are checked systematically. Instead, we define a cube-to-cube connection based upon geometrical closeness provided by convex hulls computation. We further evaluate the precision of 3D models reconstructed from synthetic and real data obtained in confocal microscopy and compare it with the conventional MC algorithm. We also discuss improvements that allow to reduce the number of generated surface patches and the ability to be used in 3D quantitative tasks.     

Colour Helmholtz Stereopsis for Reconstruction of Dynamic Scenes with Arbitrary Unknown Reflectance

Helmholtz Stereopsis is a powerful technique for reconstruction of scenes with arbitrary reflectance properties. However, previous formulations have been limited to static objects due to the requirement to sequentially capture reciprocal image pairs (i.e. two images with the camera and light source positions mutually interchanged). In this paper, we propose colour Helmholtz Stereopsis—a novel framework for Helmholtz Stereopsis based on wavelength multiplexing. To address the new set of challenges introduced by multispectral data acquisition, the proposed Colour Helmholtz Stereopsis pipeline uniquely combines a tailored photometric calibration for multiple camera/light source pairs, a novel procedure for spatio-temporal surface chromaticity calibration and a state-of-the-art Bayesian formulation necessary for accurate reconstruction from a minimal number of reciprocal pairs. In this framework, reflectance is spatially unconstrained both in terms of its chromaticity and the directional component dependent on the illumination incidence and viewing angles. The proposed approach for the first time enables modelling of dynamic scenes with arbitrary unknown and spatially varying reflectance using a practical acquisition set-up consisting of a small number of cameras and light sources. Experimental results demonstrate the accuracy and flexibility of the technique on a variety of static and dynamic scenes with arbitrary unknown BRDF and chromaticity ranging from uniform to arbitrary and spatially varying.     

Motion Blur for EWA Surface Splatting

This paper presents a novel framework for elliptical weighted average (EWA) surface splatting with time‐varying scenes. We extend the theoretical basis of the original framework by replacing the 2D surface reconstruction filters by 3D kernels which unify the spatial and temporal component of moving objects. Based on the newly derived mathematical framework we introduce a rendering algorithm that supports the generation of high‐quality motion blur for point‐based objects using a piecewise linear approximation of the motion. The rendering algorithm applies ellipsoids as rendering primitives which are constructed by extending planar EWA surface splats into the temporal dimension along the instantaneous motion vector. Finally, we present an implementation of the proposed rendering algorithm with approximated occlusion handling using advanced features of modern GPUs and show its capability of producing motion‐blurred result images at interactive frame rates.     

Shape Reconstruction and Camera Self-Calibration Using Cast Shadows and Scene Geometries

Recently, various techniques of shape reconstruction using cast shadows have been proposed. These techniques have the advantage that they can be applied to various scenes, including outdoor scenes, without using special devices. Previously proposed techniques usually require calibration of camera parameters and light source positions, and such calibration processes limit the range of application of these techniques. In this paper, we propose a method to reconstruct 3D scenes even when the camera parameters or light source positions are unknown. The technique first recovers the shape with 4-DOF indeterminacy using coplanarities obtained by cast shadows of straight edges or visible planes in a scene, and then upgrades the shape using metric constraints obtained from the geometrical constraints in the scene. In order to circumvent the need for calibrations and special devices, we propose both linear and nonlinear methods in this paper. Experiments using simulated and real images verified the effectiveness of this technique.     

Three-dimensional Visual Mapping of Underwater Ship Hull Surface Using Piecewise-planar SLAM

In-water visual ship hull inspection using unmanned underwater vehicles needs to be performed at very close range to the target surface because of the visibility limitations in underwater environments mainly due to light attenuation, scattering, and water turbidity. These environmental challenges result in ineffective photometric and geometric information in hull surface images and, therefore, the performance of conventional three-dimensional (3D) reconstruction techniques is often unsatisfactory. This paper addresses a visual mapping method for 3D reconstruction of underwater ship hull surface using a monocular camera as a primary mapping sensor. The main idea of the proposed approach is to model the moderately curved hull surface as a combination of piecewise-planar panels, and to generate a global map by aligning the local images in a two-dimensional reference frame and correcting them appropriately to reflect the information of perspective projections of the 3D panels. The estimated 3D panels associated with the local images are used to extract the loop-closure relative measurements in the framework of simultaneous localization and mapping (SLAM) for precise camera trajectory estimation and 3D reconstruction results. The validity and practical feasibility of the proposed method are demonstrated using a dataset obtained in a field experiment with a full-scale ship in a real sea environment.

     

Realistic surface geometry reconstruction using a hand-held RGB-D camera

In this paper, we have proposed a novel approach for the reconstruction of real object/scene with realistic surface geometry using a hand-held, low-cost, RGB-D camera. To achieve accurate reconstruction, the most important issues to consider are the quality of the geometry information provided and the global alignment method between frames. In our approach, new surface geometry refinement is used to recover finer scale surface geometry from depth data by utilizing high-quality RGB images. In addition, a weighted multi-scale iterative closest point method is exploited to align each scan to the global model accurately. We show the effectiveness of the proposed surface geometry refinement method by comparing it with other depth refinement methods. We also show both the qualitative and quantitative results of reconstructed models by comparing it with other reconstruction methods.     

基于改进固定点迭代方法的深层活体量化光声成像

针对限制视图下光声图像的重建伪影问题，提出一种改进的固定点迭代量化光声成像方法。首先，通过传统的反投影重建算法重建由探测器探测到的原始光声压数据，得到原始的光声压图像；接着，利用自适应维纳滤波算法对原始的光声压图像进行滤波去除重建图像伪影；然后，通过光传输模型求解目标成像区域的光通量；最后，进行迭代计算，获得目标组织的光学吸收系数。此外，在求解光通量过程中引入Toast++软件来实现光传输模型的前向求解，提高量化成像的效率和精确性。仿体和活体实验结果表明，与传统固定点迭代方法相比，所提方法能够获取更高质量的光声图像，重建得到的深层量化光声图像中存在较少伪影；量化重建的深层目标组织的光学吸收系数与浅层目标组织的光学吸收系数的数值非常接近，前者约为后者的70%，能实现深层生物组织光学吸收系数的定量重建。     

Improving three-dimensional point reconstruction from image correspondences using surface curvatures

Recovering three-dimensional (3D) points from image correspondences is an important and fundamental task in computer vision. Traditionally, the task is completed by triangulation whose accuracy has its limitation in some applications. In this paper, we present a framework that incorporates surface characteristics such as Gaussian and mean curvatures into 3D point reconstruction to enhance the reconstruction accuracy. A Gaussian and mean curvature estimation scheme suitable to the proposed framework is also introduced in this paper. Based on this estimation scheme and the proposed framework, the 3D point recovery from image correspondences is formulated as an optimization problem with the surface curvatures modeled as soft constraints. To analyze the performance of proposed 3D reconstruction approach, we generated some synthetic data, including the points on the surfaces of a plane, a cylinder and a sphere, to test the approach. The experimental results demonstrated that the proposed framework can indeed improve the accuracy of 3D point reconstruction. Some real-image data were also tested and the results also confirm this point.     

反向工程中NURBS曲面CAD重构技术研究

CAD几何模型重构技术是反向工程RE(Reverse Engineering)技术的核心,NURBS(Non-Uniform Rational B-Splines)方法因其表示法的形状控制灵活性,在曲面造型和曲面重构中具有重要作用.针对CAD曲面重构技术进行研究,并对NURBS曲面重构提出一种新的构建方法,将每个数据点相对应的有理基函数的参数值最大化,所得函数值作为数据点的参数值来构造NURBS参数曲面.最后通过实例证明了该方法的有效性.     

A new algorithm for forest height estimation based on the varied extinction random volume over ground (VERVoG) model using PolInSAR data

ABSTRACT

This paper examines a simple geometrical method for forest height estimation using single-baseline single frequency polarimetric synthetic aperture radar interferometry (PolInSAR) data. The suggested method estimates the forest biophysical parameters based on the varied extinction random volume over ground (VERVoG) model with top layer extinction greater than zero. We approach the problem using a geometrical method without the need for any auxiliary data or prior information. The biophysical parameters, i.e. top layer extinction value, forest height and extinction gradient are estimated in two separate stages. In this framework, the offset value of the extinction is estimated in an independent procedure as a function of a geometrical index based on the signal penetration in the volume layer. As a result, two remaining biophysical parameters can be calculated in a geometrical way based on the observed volume coherence. The proposed algorithm was evaluated using the L-band PolInSAR data of the European Space Agency (ESA) BioSAR 2007 campaign. A pair of experimental SAR (ESAR) images was acquired over the Remningstorp test site in southern Sweden. The selected images were employed for the performance analysis of the proposed approach in the forest height estimation application based on the VERVoG model. The experimental result shows that the proposed inversion method based on the VERVoG model with top layer extinction greater than zero estimates the volume height with an average root mean square error (RMSE) of 2.08 m against light detection and ranging (LiDAR) heights. It presents a significant improvement of forest height accuracy, i.e. 4.1 m compared to the constant extinction RVoG model result, which ignores the forest heterogeneity in the vertical direction.     

A neural network architecture for understanding discrete three-dimensional scenes in medical imaging.

Magnetic resonance and computed tomography produce sets of tomograms which are termed discrete 3D scenes. Usually, discrete 3D scenes are analyzed in two dimensions by observing each tomogram on a screen so that the three-dimensional information contained in the scene can be recovered only partially and qualitatively. The three-dimensional reconstruction of the shape of biological structures from discrete 3D scenes would allow a complete and quantitative recovery of the available information, but this task has proved hard for conventional processing techniques. In this paper we present a system architecture based on neural networks for the fully automated segmentation and recognition of structures of interest in discrete 3D scenes. The system includes a retina and two main processing modules, an Attention-Focuser System and a Region-Finder System, which have been implemented by using feed-forward nets trained with the back-propagation algorithm. This architecture has been tested on computer-simulated structures and has been applied to the reconstruction of the spinal cord and the brain from sets of tomograms.     

An implicit skeleton-based method for the geometry reconstruction of vasculatures

Due to the high complexity of vascular system network, the geometry reconstruction of vasculatures from raw medical datasets remains a very challenging task. In this paper, we present a novel skeleton-based method for the geometry reconstruction of vascular structures from standard 3D medical datasets. With the proposed techniques, the geometry of vascular structures with high level of smoothness and accuracy can be reconstructed from the raw medical datasets. The experimental results and comparison with other techniques demonstrate that our method can achieve faithful and smooth vascular structures. In addition, quantitative validation has been conducted to evaluate the accuracy and smoothness of the reconstructed vessel geometry based on the proposed method.     

Optimal Reconstruction of Approximate Planar Surfaces Using Photometric Stereo

Photometric stereo can be used to obtain a fast and noncontact surface reconstruction of Lambertian surfaces. Despite several published works concerning the uncertainties and optimal light configurations of photometric stereo, no solutions for optimal surface reconstruction from noisy real images have been proposed. In this paper, optimal surface reconstruction methods for approximate planar textured surfaces using photometric stereo are derived, given that the statistics of imaging errors are measurable. Simulated and real surfaces are experimentally studied, and the results validate that the proposed approaches improve the surface reconstruction especially for the high-frequency height variations.     

A general framework for three-dimensional surface reconstruction by self-consistent fusion of shading and shadow features

In this paper a novel framework for three-dimensional surface reconstruction by self-consistent fusion of shading and shadow features is presented. Based on the analysis of at least two pixel-synchronous images of the scene under different illumination conditions, this framework combines a shape from shading approach for estimating surface gradients and altitude variations on small scales with a shadow analysis method that allows for the determination of the large-scale properties of the surface. As a first step, the result of shadow analysis is used for selecting a consistent solution of the shape from shading reconstruction algorithm. As a second step, an additional error term derived from the fine-structure of the shadow is incorporated into the reconstruction algorithm. This approach is extended to the analysis of two or more shadows under different illumination conditions leading to an appropriate initialization of the shape from shading algorithm. The framework is applied to the astrogeological task of three-dimensional reconstruction of regions on the lunar surface using ground-based CCD images and to the machine vision task of industrial quality inspection.     

Advanced automatic grouping for form-finding of tensegrity structures

An advanced automatic grouping method for form-finding of tensegrity structures is presented. In the proposed method, properties of self-equilibrium and stability in tensegrity structures can be obtained by using the force density method combined with a genetic algorithm. A constrained minimization problem is formulated using the standard deviation of the force density in the cables. As a result, the minimum number of member groups for tensegrity structures with automatic grouping can be obtained. This elicited regular tensegrity structures with uniform force density values. Moreover, the geometrical and mechanical parameters of tensegrity structures with multiple states of self-stress can be easily obtained by using the proposed method.     

Unsupervised Image Reconstruction for Gradient-Domain Volumetric Rendering

Gradient-domain rendering can highly improve the convergence of light transport simulation using the smoothness in image space. These methods generate image gradients and solve an image reconstruction problem with rendered image and the gradient images. Recently, a previous work proposed a gradient-domain volumetric photon density estimation for homogeneous participating media. However, the image reconstruction relies on traditional L1 reconstruction, which leads to obvious artifacts when only a few rendering passes are performed. Deep learning based reconstruction methods have been exploited for surface rendering, but they are not suitable for volume density estimation. In this paper, we propose an unsupervised neural network for image reconstruction of gradient-domain volumetric photon density estimation, more specifically for volumetric photon mapping, using a variant of GradNet with an encoded shift connection and a separated auxiliary feature branch, which includes volume based auxiliary features such as transmittance and photon density. Our network smooths the images on global scale and preserves the high frequency details on a small scale. We demonstrate that our network produces a higher quality result, compared to previous work. Although we only considered volumetric photon mapping, it's straightforward to extend our method for other forms, like beam radiance estimation.