We present a system to improve the robustness of real-time 3D surface reconstruction by utilizing non-inertial localization sensor. Benefiting from such sensor, our easy-to-build system can effectively avoid tracking drift and lost comparing with conventional dense tracking and mapping systems. To best fusing the sensor, we first adopt a hand-eye calibration and performance analysis for our setup and then propose a novel optimization framework based on adaptive criterion function to improve the robustness as well as accuracy. We apply our system to several challenging reconstruction tasks, which show significant improvement in scanning robustness and reconstruction quality.

The three dimensional (3D) reconstruction of a medical image sequence can provide intuitive morphologies of a target and help doctors to make more reliable diagnosis and give a proper treatment plan. This paper aims to reconstruct the surface of a renal corpuscle from the microscope renal biopsy image sequence. First, the contours of renal corpuscle in all slices are extracted automatically by using a context-based segmentation method with a coarse registration. Then, a new coevolutionary-based strategy is proposed to realize a fine registration. Finally, a Gauss-Seidel iteration method is introduced to achieve a non-rigid registration. Benefiting from the registrations, a smooth surface of the target can be reconstructed easily. Experimental results prove that the proposed method can effectively register the contours and give an acceptable surface for medical doctors.

This paper describes a robust three-dimensional (3D) surface reconstruction method that can automatically eliminate shadowing errors. For modeling shadowing effect, a new shadowing compensation model based on the angle distribution of backscattered electrons is introduced. Further, it is modified with respect to some practical factors. Moreover, the proposed iterative shadowing compensation method, which performs commutatively between the compensation of image intensities and the modification of the corresponding 3D surface, can effectively provide both an accurate 3D surface and compensated shadowless images after convergence.

In this letter, we present an efficient method for high quality surface reconstruction from simulation data of smoothed particles hydrodynamics (SPH). For computational efficiency, instead of computing scalar field in overall particle sets, we only construct scalar field around fluid surfaces. Furthermore, an adaptive scalar field model is proposed, which adaptively adjusts the smoothing length of ellipsoidal kernel by a constraint-correction rule. Then the isosurfaces are extracted from the scalar field data. The proposed method can not only effectively preserve fluid details, such as splashes, droplets and surface wave phenomena, but also save computational costs. The experimental results show that our method can reconstruct the realistic fluid surfaces with different particle sets.

This paper proposes a method for reconstructing a smooth and accurate 3D surface. Recent machine vision techniques can reconstruct accurate 3D points and normals of an object. The reconstructed point cloud is used for generating its 3D surface by surface reconstruction. The more accurate the point cloud, the more correct the surface becomes. For improving the surface, how to integrate the advantages of existing techniques for point reconstruction is proposed. Specifically, robust and dense reconstruction with Shape-from-Silhouettes (SfS) and accurate stereo reconstruction are integrated. Unlike gradual shape shrinking by space carving, our method obtains 3D points by SfS and stereo independently and accepts the correct points reconstructed. Experimental results show the improvement by our method.

A new hierarchical isosurface reconstruction scheme from a set of tomographic cross sectional images is presented. From the input data, we construct a hierarchy of volume, called the volume pyramid, based on a 3D dilation filter. After extracting the base mesh from the volume at the coarsest level by the cell-boundary method, we iteratively fit the mesh to the isopoints representing the actual isosurface of the volume. The SWIS (Shrink-wrapped isosurface) algorithm is adopted in this process, and a mesh subdivision scheme is utilized to reconstruct fine detail of the isosurface. According to experiments, our method is proved to produce a hierarchical isosurface which can be utilized by various multiresolution algorithms such as interactive visualization and progressive transmission.

This paper addresses a new surface reconstruction scheme for approximating the isosurface from a set of tomographic cross sectional images. Differently from the novel Marching Cubes (MC) algorithm, our method does not extract the iso-density surface (isosurface) directly from the voxel data but calculates the iso-density point (isopoint) first. After building a coarse initial mesh approximating the ideal isosurface by the cell-boundary representation, it metamorphoses the mesh into the final isosurface by a relaxation scheme, called shrink-wrapping process. Compared with the MC algorithm, our method is robust and does not make any cracks on surface. Furthermore, since it is possible to utilize lots of additional isopoints during the surface reconstruction process by extending the adjacency definition, theoretically the resulting surface can be better in quality than the MC algorithm. According to experiments, it is proved to be very robust and efficient for isosurface reconstruction from cross sectional images.

In the present paper, we propose a method for reconstructing the surfaces of objects from stereo data. Both the fitness of stereo data to surfaces and interrelation between the surfaces are defined in the framework of a three-dimensional (3-D) Markov Random Field (MRF) model. The surface reconstruction is accomplished by searching for the most likely state of the MRF model. Three experimental results are shown for synthetic and real stereo data.

We propose using SQP (Sequential Quadratic Programming) to directly recover 3D quadratic surface parameters from multiple views. A surface equation is used as a constraint. In addition to the sum of squared reprojection errors defined in the traditional bundle adjustment, a Lagrangian term is added to force recovered points to satisfy the constraint. The minimization is realized by SQP. Our algorithm has three advantages. First, given corresponding features in multiple views, the SQP implementation can directly recover the quadratic surface parameters optimally instead of a collection of isolated 3D points coordinates. Second, the specified constraints are strictly satisfied and the camera parameters and 3D coordinates of points can be determined more accurately than that by unconstrained methods. Third, the recovered quadratic surface model can be represented by a much smaller number of parameters instead of point clouds and triangular patches. Experiments with both synthetic and real images show the power of this approach.

3D model-based coding methods that need 3D reconstruction techniques are proposed for next-generation image coding methods. A method is presented that reconstructs 3D shapes of dynamic objects from image sequences captured using two cameras, thus avoiding the stereo correspondence problem. A coaxial camera system consisting of one moving and one static camera was developed. The optical axes of both cameras are precisely adjusted and have the same orientation using an optical system with true and half mirrors. The moving camera is moved along a straight horizontal line. This method can reconstruct 3D shapes of static objects as well as dynamic objects using motion vectors calculated from the moving camera images and revised using the static camera image. The method was tested successfully on real images by reconstructing a moving human shape.

Three dimensional model based coding methods are proposed as next generation image coding methods. These new representations need 3D reconstruction techniques. This paper presents a method that extracts the surfaces of static objects that occlude other objects from a spatiotemporal image captured with straight-line camera motion. We propose the concept of occlusion types and show that the occlusion types are restricted to only eight patterns. Furthermore, we show occlusion type pairs contain information that confirms the existence of surfaces. Occlusion information gives strong cues for segmentation and representation. The method can estimate not only the 3D positions of edge points but also the surfaces bounded by the edge points. We show that combinations of occlusion types contain information that can confirm surface existence. The method was tested successfully on real images by reconstructing flat and curved surfaces. Videos can be hierarchically structured with the method. The method makes various applications possible, such as object selective image communication and object selective video editing.

This paper addresses a method for constructing surface representation of 3D structures from a sequence of cross-sectional images. Firstly, we propose cell-boundary representation, which is a generalization of PVP method proposed by Yun and Park, and develop an efficient surface construction algorithm from a cell-boundary. Cell-boundary consists of a set of boundary cells with their 1-voxel configurations, and can compactly describe binary volumetric data. Secondly, to produce external surface from the cell-boundary representation, we define 19 modeling primitives (MP) including volumetric, planar and linear groups. Surface polygons are created from those modeling primitives using a simple table look-up operation. Since a cell-boundary can be obtained using only topological information of neighboring voxels, there is no ambiguity in determining modeling primitives which may arise in PVP method. Since our algorithm has data locality and is very simple to implement, it is very appropriate for parallel processing.

Surface reconstruction and visualization from sparse and incomplete surface data is a fundamental problem and has received growing attention in both computer vision and graphics. This paper presents a computational scheme for realistic visualization of free-formed surfaces from 3D range images. The novelty of this scheme is that by integrating computer vision and computer graphics techniques, we dynamically construct a mesh representation of the arbitrary view of the surfaces, from a view-invariant shape description obtained from 3D range images. We outline the principle of this scheme and describle the frame work of a graphical reconstruction model, we call arbitrarily oriented meshes', which is developed based on differential geometry. The experimental results on real range data of human faces are shown.