Ego-Motion Estimation on Range Images Using High-Order Polynomial Expansion

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.

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.

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.

In Versatile Video Coding (VVC), local affine motion compensation (LAMC) is adopted to handle complex motions, such as rotation and zooming. However, it is inefficient to use LAMC to handle the global motion due to the following two reasons. First, the use of LAMC may lead to some extra bit cost on the affine motion model parameters. Second, the precision of LAMC is restricted by the MV precision of the control points. Therefore, in this paper, we propose a global homography motion compensation (GHMC) framework to better characterize the global motion. For each coding block, an extra mode is added to perform motion compensation based on an 8-parameter global homography motion model. In addition, an extrapolation scheme is designed to derive the parameters from reference frames to save the bit cost for signaling them. The proposed framework is implemented into the VVC reference software VTM-6.0. Experimental results show that, on average, 0.69% and 0.66% BD-rate reduction is achieved under Low Delay P and Low Delay B configurations, respectively, for sequences with rich complex global motions.

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

Based on the spherical harmonics representation of image formation, we derive a new photometric metric for evaluating the correctness of a given rigid transformation aligning two overlapping range images captured under unknown, distant, and general illumination. We estimate the surrounding illumination and albedo values of points of the two range images from the point correspondences induced by the input transformation. We then synthesize the color of both range images using albedo values transferred using the point correspondences to compute the photometric reprojection error. This way allows us to accurately register two range images by finding the transformation that minimizes the photometric reprojection error. We also propose a practical method using the proposed photometric metric to register pairs of range images devoid of salient geometric features, captured under unknown lighting. Our method uses a hypothesize-and-test strategy to search for the transformation that minimizes our photometric metric. Transformation candidates are efficiently generated by employing the spherical representation of each range image. Experimental results using both synthetic and real data demonstrate the usefulness of the proposed metric.

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.

In this paper, we present an improved approach to predictive video decoding based on global and local motion reliability. The framework consists of three processing stages. Global motion (GM) estimation and motion reliability analysis are the key components in the first stage, where we model global motion and refine the MV field. In the second stage, we predict local and global motion for the target frame, and determine corresponding weights based on the Linear Minimum Mean Square Error (LMMSE) criterion. Finally in the third stage, a temporal interpolator is applied to compose two future frames, which are linearly combined to form the final predicted frame. Our results indicate the proposed method achieves better visual quality compare to other state-of-the-art predictive decoding approaches, particularly in sequences involving moving camera and objects.

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.

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.

We have investigated the global and local motion tuning properties of curvature coding mechanisms using two shape after-effects believed to be mediated by curvature-sensitive mechanisms: the shape-frequency after-effect, or SFAE, and the shape-amplitude after-effect, or SAAE. The SFAE and SAAE are the phenomena in which adaptation to a sine-wave-shaped contour causes a shift in respectively the apparent shape-frequency and apparent shape-amplitude of a test contour in a direction away from that of the adapting stimulus. In the global motion condition the sinusoidal-shaped contours were made to drift within a fixed stimulus window in the direction of their axis of modulation. In the local motion condition the contour was constructed from a string of Gabors, and their carriers but not envelopes moved. We investigated selectivity to motion direction by using adaptor and test contours that moved either in the same or opposite directions. We found that in the global motion condition both the SFAE and SAAE showed selectivity to motion direction, and that for the same-motion-direction condition, both after-effects increased with shape temporal frequency. We then examined the effect of luminance spatial frequency and luminance temporal frequency on global motion direction selectivity. Luminance temporal frequency accounted for some of the increase in after-effect magnitude with shape temporal frequency, but shape temporal frequency also contributed. The local motion after-effects on the other hand were neither selective to motion direction nor increased with luminance temporal frequency. Taken together, the results are best understood by supposing that curvature is encoded by mechanisms that are selective to motion direction and that the directional selectivity best manifests itself psychophysically when there is sufficient spatio-temporal coverage of the stimulus to stimulate the full array of potentially responsive curvature-coding mechanisms.

A shape descriptor and a complete shape-based recognition system using slice images as geometric feature descriptor for ladar range images are introduced. A slice image is a two-dimensional image generated by three-dimensional Hough transform and the corresponding mathematical transformation. The system consists of two processes, the model library construction and recognition. In the model library construction process, a series of range images are obtained after the model object is sampled at preset attitude angles. Then, all the range images are converted into slice images. The number of slice images is reduced by clustering analysis and finding a representation to reduce the size of the model library. In the recognition process, the slice image of the scene is compared with the slice image in the model library. The recognition results depend on the comparison. Simulated ladar range images are used to analyze the recognition and misjudgment rates, and comparison between the slice image representation method and moment invariants representation method is performed. The experimental results show that whether in conditions without noise or with ladar noise, the system has a high recognition rate and low misjudgment rate. The comparison experiment demonstrates that the slice image has better representation ability than moment invariants.

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. >

Analysis and reconstruction of range images usually focuses on complex objects completely contained in the field of view; little attention has been devoted so far to the reconstruction of partially occluded simple-shaped wide areas like parts of a wall hidden behind furniture pieces in an indoor range image. The work in this paper is aimed at such reconstruction. First of all the range image is partitioned and surfaces are fitted to these partitions. A further step locates possibly, occluded areas, while a final step determines which areas are actually occluded. The reconstruction of data occurs in this last step.