Robust Automatic Registration of Range Images with Reflectance

Marcel Körtgen
Computer Graphics Group, Institute of Computer Science II, University of Bonn/Germany


We tackle the problem of automatic matching, consistency checking and registration of multiple unknown and unordered range images. Utilizing robust visual features extracted from the supplied reflectance images, an efficient pairwise view matching scheme is used to build up a directed correspondence graph, nodes representing the input range images and edges labeled with relative pose estimates. Subsequently, a local and global consistency check eliminate false positive edges in the graph as these prevent the succeeding to a correct solution. Absolute poses are recovered by a breadth-first search (BFS), thereby, for each visited node, combining the weighted contributions of all encountered paths back to the root. Remarkably, the absolute alignments are accurately recovered from only the features. Thus, a subsequent fine registration step can be omitted. The framework is independent from object size and particular sensor model.


Registration has been an active topic of research for about thirty years. Much work in the past successfully addressed pairwise registration, i.e. aligning two three-dimensional (3d) views of a static scene. Due to the ongoing advances in scanning and computer hardware for about the last ten years, multiple view registration became manageably and thus more and more attractive as a basic tool in reconstructing a complete 3d model from a captured scene. However, most multiview approaches assume that the input views are roughly prealigned or that it is known which views overlap one another. In contrast, just a small amount of research has been published that engages automatic matching, consistency checking and registration of multiple unknown views as presented here. Today, a laser range scanner is the method of choice for digitizing real-world objects of moderate size. Laser range scanners are non-contact 3d scanners that measure the distance from the sensor to points in the scene, typically in a regular grid pattern. A range image is the visualization of this grid pattern where the pixel intensity is a function of the measured distance (cf. figure 1). A natural byproduct of the acquisition process is the reflectance image that records the laser reflectance strength (LRS) for each pixel.

Figure 1: a) A range image (left) obtained with the Z+F Imager 5003 and corresponding co-registered reflectance image (right). b) A range image (left) obtained with the Minolta Vivid 900 and gray-scaled color image (right).

In general, because of occlusion and field of view limitations, not all parts of the scene can be observed from any given position. Therefore, range data from multiple viewpoints must be combined to form a complete model of the scene. Given a set of n overlapping range images of a static scene, the process of creating the complete scene model consists of two main steps: registration and reconstruction. In the registration step, the n input range images are all aligned in a common coordinate system whereas the reconstruction step usually accounts for the generation of a triangulated mesh out of the registered range data. We concentrate on the registration step because this is where the central automation issues lie.


Download the full paper: paper.pdf.
Download the bibtex-file: paper.bib.

Images and Results

Some images and results not shown in the paper (See [16] instead).
Spherical Scans (Z+F Imager 5003)

Resampling to 8 overlapping perspective range images.

6 spherical scans registered (48 range images; 512x512 resolution each).
Here we considered a large industrial indoor scene consisting of 6 spherical scans acquired with the Z+F 3D Imager 5003, a high-accuracy laser range scanner based on the phase-measurement principle. The device provides true reflectances and has a nominal error in accuracy of less than 5 mm at a maximum distance of 53.5 m. Each view consists of a full spherical range image (360° x 180°) with a resolution of 10.134 x 5.000 = 550.670.000 samples, parameterized over the unit sphere.
However, by the time of writing the implementation of the SIFT detector [25] ( was neither suitable for non-perspective images nor resolutions of more than about 1.800 pixels in any dimension. We therefore partition the spherical images into n=2x4 perspective range images (512x512 each), by sequential rotations of 45° about the up-vector and subsequent projection on an image plane with a 90° field of view, both horizontally and vertically. In order to avoid missing features or cutting them in two, each perspective view overlaps half of the adjacent views. In this manner, each spherical view is decomposed into a graph component of 8 perspective range images that are already registered with respect to the considered spherical view.

Observation confidence for range measurements

Dichromatic reflection model as proposed by [32].

Contour plots of conditional pixel confidences.
In laser range scanning, the surface is typically assumed to be matte and therefore follows a Lambertian reflection model [32]. Laser light is known to have a very narrow wavelength distribution and it is therefore common practice to consider it as light of a single wavelength. In this case, a suitable reflectance model is the dichromatic reflection model. Basically, the only kinds of reflection occurring in this model are specular and diffuse reflection. Sagawa et al. [32] justify that diffuse reflection dominates the appearance of reflectance images. They propose a model which combines Lambertian diffuse reflection and exponential absorption by the transport medium (usually air).
For a surface (a pixel) observed in range image A, the likelihood of observation is modeled as a gaussian of the measured reflectance. The location parameter (or the mean) of the gaussian represents the calibrated reflectance of the device.
The scale parameter (or the standard deviation) represents the range of reflectances around the mean that can be confidently handled by the device. For reflectances higher than the mean one needs to consider blooming whereas the case for reflectances lower than the mean is suspect to undersaturation.
When surface/point normals are available the dichromatic reflectance model can be used to infer the "warped" reflectance, i.e. the reflectance of the same pixel as seen from the viewpoint of range image B. Feeding this warped reflectance value into the gaussian yields the conditional observation confidence P(A|B), which is shown above for varying normal-eye-angles and measured reflectances in image A. Note that reflectances are scaled to [0,1].
The question remains how to select proper values for location and scale of the gaussian. If the device is well known or directly available, these values can be derived from the technical specifications or obtained by a calibration experiment. Otherwise, we propose maximum-likelihood estimation from an accumulative reflectance histogram of the input set.

Probability distribution function for local consistency

For inconsistent scan matches, the number, locations and heights of peaks is probably unpredictable.

For consistent matches the histogram of surface distances follows a unimodal distribution.
In order to select a suitable model for binary local consistency classification, we considered histograms of several hundred example matches from categorical different input. The images above show example reflectance images of two matched scans (top) as well as the corresponding 8-bit quantized range difference images Delta(Vj,Vi) and Delta(Vi,Vj) (bottom). The 256-bin histograms to the right approximate the distribution of the observed surface distances between to matched range images Vj,W(Vi) (blue) and Vi,W(Vj) (green), where W denotes a warping operator. Our observations for both inconsistent (left) and consistent matches (right) can be summarized as follows:


Download the real-time rendering video of the registered 3d pointcloud shown in the paper:

(c) Marcel Körtgen


Download the bibtex-file: paper.bib

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