Online 3D reconstruction is gaining newfound interest due to the availability of real-time consumer depth cameras. The basic problem takes live overlapping depth maps as input and incrementally fuses these into a single 3D model. This is challenging particularly when real-time performance is desired without trading quality or scale. We contribute an online system for large and fine scale volumetric reconstruction based on a memory and speed efficient data structure. Our system uses a simple spatial hashing scheme that compresses space, and allows for real-time access and updates of implicit surface data, without the need for a regular or hierarchical grid data structure. Surface data is only stored densely where measurements are observed. Additionally, data can be streamed efficiently in or out of the hash table, allowing for further scalability during sensor motion. We show interactive reconstructions of a variety of scenes, reconstructing both fine-grained details and large scale environments. We illustrate how all parts of our pipeline from depth map pre-processing, camera pose estimation, depth map fusion, and surface rendering are performed at real-time rates on commodity graphics hardware. We conclude with a comparison to current state-of-the-art online systems, illustrating improved performance and reconstruction quality.

/pdf/real-time-3d-reconstruction-at-scale-using-voxel-hashing-41a8l8088q.pdf

Real-time 3D reconstruction at scale using voxel hashing

We develop two algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment The first algorithm is used on matching tangent lines defined on two scans and minimizing a distance function The second algorithm iteratively establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose Our methods avoid the use of localized features They work in curved environments and can handle partial occlusions >

Robot pose estimation in unknown environments by matching 2D range scans

A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses two-dimensional laser range scans for localization, it is difficult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans.

In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The first algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacement between the scans. The second algorithm establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose of the two scans. Our methods work in curved environments and can handle partial occlusions by rejecting outliers.

Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans

Investigates the use of Euclidean invariant features in a generalization of iterative closest point (ICP) registration of range images. Pointwise correspondences are chosen as the closest point with respect to a weighted linear combination of positional and feature distances. It is shown that, under ideal noise-free conditions, correspondences formed using this distance function are correct more often than correspondences formed using the positional distance alone. In addition, monotonic convergence to at least a local minimum is shown to hold for this method. When noise is present, a method that automatically sets the optimal relative contribution of features and positions is described. This method trades off the error in feature values due to noise against the error in positions due to misalignment. Experimental results suggest that using invariant features decreases the probability of being trapped in a local minimum and may be an effective solution for difficult range image registration problems where the scene is very small compared to the model.

ICP registration using invariant features

The automatic co-registration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. This multiple registration problem can be defined as a surface matching task. We treat it as least squares matching of overlapping surfaces. The surface may have been digitized/sampled point by point using a laser scanner device, a photogrammetric method or other surface measurement techniques. Our proposed method estimates the transformation parameters of one or more 3D search surfaces with respect to a 3D template surface, using the Generalized Gauss–Markoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulation gives the opportunity of matching arbitrarily oriented 3D surface patches. It fully considers 3D geometry. Besides the mathematical model and execution aspects we address the further extensions of the basic model. We also show how this method can be used for curve matching in 3D space and matching of curves to surfaces. Some practical examples based on the registration of close-range laser scanner and photogrammetric point clouds are presented for the demonstration of the method. This surface matching technique is a generalization of the least squares image matching concept and offers high flexibility for any kind of 3D surface correspondence problem, as well as statistical tools for the analysis of the quality of final matching results.

http://www2.isikun.edu.tr/personel/akca/devrim/2005__LS3D_ISPRSJfinal_vv.PDF

Least squares 3D surface and curve matching

Building 3-D models from unregistered range images

The authors describe an approach to building a three-dimensional model from a set of range images. The authors' goal is to build models of free-form surfaces obtained from arbitrary viewing directions, with no initial estimate of the relative viewing directions. The approach is based on building discrete meshes representing the surfaces observed in each of the range images, to map each of the meshes to a spherical image, and to compute the transformations between the views by matching the spherical images. The meshes are built using an iterative fitting algorithm previously developed; the spherical images are built by matching the nodes of the surface meshes to the nodes of a reference mesh on the unit sphere and by storing a measure of curvature at every node. The authors describe the algorithms used for building such models from range images and for matching them. The authors give results obtained using range images of complex objects. >

An XYZ coordinates measuring system has a slit light source for projecting a slit light beam onto the surface of a work to be measured with a predetermined angle. Furthermore a television camera takes a light line formed on the surface of the work by the slit light beam to generate an output video signal. A coordinate measuring circuit has a look-up table for previously storing the interrelationship between the horizontal and vertical imaging positions of the light line and the XYZ coordinates on the surface of the actual work. The coordinate measuring circuit is adapted to process the video signal to detect the horizontal and vertical imaging positions Ks and Ls of the light line and to output the XYZ coordinates on the surface of the work from the look-up table, depending on the detected horizontal and vertical imaging positions Ks and Ls, the XYZ coordinates on the surface of the work can thus be real-time measured along the light line.

XYZ coordinates measuring system

A system for measuring three-dimensional shapes and dimensions includes: an imaging unit which faces an object with shape changes on the surface and images by means of a TV camera the slit line formed on the object surface by projecting slit light from a slit light source at a fixed angle; and a surface feature extraction member for extracting features representing the shape changes of the object surface, based on the changes of the XYZ coordinate values of the object surface detected by triangulation based on the centroid position of the intensity distribution of the slit line image corresponding to the scanning lines from the TV camera. Based on features thus extracted, the surface shape of the object can be measured rapidly with high precision.

System for measuring shapes and dimensions of gaps and flushnesses on three dimensional surfaces of objects

PROBLEM TO BE SOLVED: To provide a light irradiation device for irradiating light an object to be noticed under a state where the object to be noticed can take a notice in an easy manner against a presence of a driver's own-vehicle without being dependent on any positional relation between the own-vehicle and the object to be noticed SOLUTION: An irradiation device 10 irradiates light toward an object to be noticed when the object is present in front of the own-vehicle 100 The irradiation device 10 includes: output means 12, 14 and 24 for outputting position of the object to be noticed so as to specify and output the position of the object when the object is present in front of the own-vehicle 100; and an irradiation means 20 for irradiating light toward the positions output by the output means 12, 14 and 24 for outputting position of the object while its optical characteristic is being changed in relation to time COPYRIGHT: (C)2009,JPO&INPIT

Kazunori Higuchi

Papers

Building 3-D models from unregistered range images

Building 3-D models from unregistered range images

XYZ coordinates measuring system

System for measuring shapes and dimensions of gaps and flushnesses on three dimensional surfaces of objects

Light irradiation device