Range Image Registration Using a Photometric Metric under Unknown Lighting

A technique to generate a photorealistic three-dimensional (3D) image and color-textured model using a dedicated optical digitizer is presented. The proposed technique is started with the range and texture image acquisition from different viewpoints, followed by the registration and integration of multiple range images to get a complete and nonredundant point cloud that represents a real-life object. The accuracy of the range image and the precision of correspondence between the range image and texture image are guaranteed by sensor system calibration. Based on the point cloud, a geometric model is established by considering the connectivity of adjacent range image points. In order to enhance the photorealistic effect, we suggest a texture blending technique that utilizes a composite-weight strategy to blend the texture images within the overlapped region. This technique allows more efficient removal of the artifacts existing in the registered texture image, leading to a 3D image with photorealistic quality and color-texture modeling. Experimental results are also presented to testify to the validity of the proposed method.

An on-line 3D registration system is introduced to reconstruct a 3D model using a hand-held stereo camera. Multi-view range images, which are obtained from the hand-held stereo camera, are registered into a reference coordinate system simultaneously. For the coarse registration of a range image, we use the transformation of its previous frame and the centroid of foreground range images. After refining the coarse registration using a geometric feature matching technique, we use a modified KLT (Kanade-Lucas-Tomasi) tracker to match photometric features to enhance registration accuracy. We modify the KLT tracker to facilitate searching of correspondence from the results of the geometric registration. If a range image fails to register, we adjust the orientation of the camera while viewing a graphical user interaction system. After enough range images are registered, they are integrated into a 3D model in offline. Experimental results show that the proposed method can be used to reconstruct 3D models fast and accurately.

Range image registration and surface reconstruction have been traditionally considered as two independent problems where the latter relies on the results of the for-mer. This paper presents a new approach to surface recov-ery from range images where these two processes are inte-grated and performed in a common volumetric representa-tion. The volumetric representation contains both implicitly represented reconstructed surface as the signed distance field and corresponding matching information in the form of the gradient of the distance field. This allows both simul-taneous and incremental registration where matching com-plexity is linear with respect to the number of images. This improvement leads to incremental modeling from range im-age acquisition to surface reconstruction. It is shown that the approach is tolerant to initial registration errors as well as to measurement errors while keeping the details of the initial range images. The paper describes the formalism of the approach. Experimental results demonstrate perfor-mance advantages, tolerance to aforementioned types of er-rors and, as an application, filtering using redundant range data without loss of sharp details on the reconstructed sur-face. 1

Multi-view range image registration is a significant and challenging problem for 3D modeling. This paper presents a reference shape based multi-view range image registration algorithm. First, a set of Rotational Projection Statistics (RoPS) features are extracted from the input range images. Next, the reference shape is initialized by selecting a range image from the input. The reference shape is then iteratively updated by registering itself with the remaining range images. The registration between the reference shape and any range image is completed by RoPS feature matching. Finally, all input range images are registered according to their corresponding reference shapes. A number of experiments were performed to test the performance of our algorithm. The experimental results show that the reference shape based algorithm can perform multi-view registration on a mixed set of unordered range images corresponding to several different objects. It is also very accurate and efficient. It outperformed the state-of-the-arts including the spanning tree based and connected graph based algorithms.

This paper considers the segmentation of range image measurements into surface patches which are either plane or curved and which are described formally by a function. After a formal description of the segmentation, we present and compare three methods suited for plane and curved patch segmentation and show the results of experiments conducted for testing their practical behaviour. The two first methods use the classical approach of region growing whereas the third method is based on a relaxation process. This original and last method exhibits simplicity and low computational complexity. Thanks to its parallel nature, it can be considered as a good candidate for range image segmentation in real-time applications.

Publication in the conference proceedings of EUSIPCO, Rhodes, Greece, 1998

An adaptive range image coding algorithm for the geometry compression of large-scale 3D point clouds (LS3DPCs) is proposed in this work. A terrestrial laser scanner generates an LS3DPC by measuring the radial distances of objects in a real world scene, which can be mapped into a range image. In general, the range image exhibits different characteristics from an ordinary luminance or color image, and thus the conventional image coding techniques are not suitable for the range image coding. We propose a hybrid range image coding algorithm, which predicts the radial distance of each pixel using previously encoded neighbors adaptively in one of three coordinate domains: range image domain, height image domain, and 3D domain. We first partition an input range image into blocks of various sizes. For each block, we apply multiple prediction modes in the three domains and compute their rate-distortion costs. Then, we perform the prediction of all pixels using the optimal mode and encode the resulting prediction residuals. Experimental results show that the proposed algorithm provides significantly better compression performance on various range images than the conventional image or video coding techniques

Time-of-flight range imaging cameras measure distance to objects in their field of view, but are prone to error when objects move. At least three raw frames are required to obtain one range image, and the standard method is to read out raw frames into separate sets and process to find one range image per set. Motion during the acquisition of a set causes error in the corresponding range image. In this paper, the problem of motion is addressed by regarding the raw data from each pixel as a noisy time series, and using the Kalman filter to efficiently perform time-series analysis. The proposed method adapts to the effects of transverse motion, measuring a sharp range image at each raw frame. The error in the proposed method is less than the traditional approach in 80% of tests, with no detected increase in the STD due to noise. In the qualitative experimental results, the visible blur is reduced.

Despite the ubiquitous use of range images in various computer vision applications, little has been investigated about the size variation of the local geometric structures captured in the range images. In this paper, we show that, through canonical geometric scale-space analysis, this geometric scale-variability embedded in a range image can be exploited as a rich source of discriminative information regarding the captured geometry. We extend previous work on geometric scale-space analysis of 3D models to analyze the scale-variability of a range image and to detect scale-dependent 3D features – geometric features with their inherent scales. We derive novel local 3D shape descriptors that encode the local shape information within the inherent support region of each feature. We show that the resulting set of scale-dependent local shape descriptors can be used in an efficient hierarchical registration algorithm for aligning range images with the same global scale. We also show that local 3D shape descriptors invariant to the scale variation can be derived and used to align range images with significantly different global scales. Finally, we demonstrate that the scale-dependent/invariant local 3D shape descriptors can even be used to fully automatically register multiple sets of range images with varying global scales corresponding to multiple objects.KeywordsGeodesic DistanceShape DescriptorRange ImageRegistration AlgorithmAutomatic RegistrationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Computer vision researchers have been frustrated in their attempts to automatically derive depth information from conventional two-dimensional intensity images. Research on shape from texture, shape from shading, and shape from focus is still in a laboratory stage and had not seen much use in commercial machine vision systems. A range image or a depth map contains explicit information about the distance from the sensor to the object surfaces within the field of view in the scene. Information about surface geometry which is important for, say, three-dimensional object recognition is more easily extracted from 2 1/2 D range images than from 2D intensity images. As a result, both active sensors such as laser range finders and passive techniques such as multi-camera stereo vision are being increasingly utilized by vision researchers to solve a variety of problems. This book contains chapters written by distinguished computer vision researchers covering the following areas: Overview of 3D Vision Range Sensing Geometric Processing Object Recognition Navigation Inspection Multisensor Fusion A workshop report, written by the editors, also appears in the book. It summarizes the state of the art and proposes future research directions in range image sensing, processing, interpretation, and applications. The book also contains an extensive, up-to-date bibliography on the above topics. This book provides a unique perspective on the problem of three-dimensional sensing and processing; it is the only comprehensive collection of papers devoted to range images. Both academic researchers interested in research issues in 3D vision and industrial engineers in search of solutions to particular problems will find this a useful reference book.

This paper presents two novel algorithms for estimating the (local and global) motion in a series of range images based on a polynomial expansion. The use of polynomial expansion has been quite successful in estimating optical flow in 2D imagery, but has not been used extensively in 3D or range imagery. In both methods, each range image is approximated by applying a high-order polynomial expansion to local neighborhoods within the range image. In the local motion algorithm, these approximations are then used to derive the translation or displacement estimation within the local neighborhoods from frame to frame within the series of range images (also known as range image flow). An iterative method for computing the local translations is presented. In the global motion algorithm, a global motion model framework is utilized to compute a global motion estimation based on the polynomial expansion of the range images. We evaluate the algorithms on several real-world range image sequences with promising results.

Range image recognition gains importance in the recent years due to the developments in acquiring, displaying, and storing such data. In this paper, we present a novel method for matching range surfaces. Our method utilizes local surface properties and represents the geometry of local regions efficiently. Integrating the Scale Invariant Feature Transform (SIFT) with the shape index (SI) representation of the range images allows matching of surfaces with different scales and orientations. We apply the method for scaled, rotated, and occluded range images and demonstrate the effectiveness it by comparing the previous studies.

Abstract-In order to eliminate the effect of the facial expression and illumination condition, as well as to speed up the recognition procedure, we propose a face recognition approach based on sparse representation. First, preprocessing and segmenting the face area from three dimensional (3D) face scans, we also apply coarse to fine registration to ensure the alignment of range images; second, mapping the 3D model to range image through a kind of geometry-based resampling method; finally, employ sparse representation classification method to identify 3D face. The experiment results in actual 3D face database demonstrate the effectiveness of the proposed method.

Range images contain 3D coordinates of points on the object surface which are sampled by range sensors. In general, a sensor can capture only parts of the object because of occlusion and sensing li...

A new method for stamped character recognition is proposed. Conventional techniques have used a camera to observe the characters and get the image as a gray-level. However, it has been difficult to get an exact image, because gray-level noise occurs at some distortion around the characters, which makes a shadow on the characters, and also at these parts of stamped surface which are dirty or painted. The approach presented can solve these problems by using a range finder system as the observation system. The method observes the stamped characters as range image. The authors compare the characteristics of a range image to the characteristics of a gray-level image, especially about the noise, and show a noise reduction method for range images. Also, the performance is verified by an experiment. >