3D Range Scans Research Articles

Fluorescent immersion range scanning

The quality of a 3D range scan should not depend on the surface properties of the object. Most active range scanning techniques, however, assume a diffuse reflector to allow for a robust detection of incident light patterns. In our approach we embed the object into a fluorescent liquid. By analyzing the light rays that become visible due to fluorescence rather than analyzing their reflections off the surface, we can detect the intersection points between the projected laser sheet and the object surface for a wide range of different materials. For transparent objects we can even directly depict a slice through the object in just one image by matching its refractive index to the one of the embedding liquid. This enables a direct sampling of the object geometry without the need for computational reconstruction. This way, a high-resolution 3D volume can be assembled simply by sweeping a laser plane through the object. We demonstrate the effectiveness of our light sheet range scanning approach on a set of objects manufactured from a variety of materials and material mixes, including dark, translucent and transparent objects.

Integrating Automated Range Registration with Multiview Geometry for the Photorealistic Modeling of Large-Scale Scenes

The photorealistic modeling of large-scale scenes, such as urban structures, requires a fusion of range sensing technology and traditional digital photography. This paper presents a system that integrates automated 3D-to-3D and 2D-to-3D registration techniques, with multiview geometry for the photorealistic modeling of urban scenes. The 3D range scans are registered using our automated 3D-to-3D registration method that matches 3D features (linear or circular) in the range images. A subset of the 2D photographs are then aligned with the 3D model using our automated 2D-to-3D registration algorithm that matches linear features between the range scans and the photographs. Finally, the 2D photographs are used to generate a second 3D model of the scene that consists of a sparse 3D point cloud, produced by applying a multiview geometry (structure-from-motion) algorithm directly on a sequence of 2D photographs. The last part of this paper introduces a novel algorithm for automatically recovering the rotation, scale, and translation that best aligns the dense and sparse models. This alignment is necessary to enable the photographs to be optimally texture mapped onto the dense model. The contribution of this work is that it merges the benefits of multiview geometry with automated registration of 3D range scans to produce photorealistic models with minimal human interaction. We present results from experiments in large-scale urban scenes.

6D SLAM—3D mapping outdoor environments

Abstract6D SLAM (simultaneous localization and mapping) or 6D concurrent localization and mapping of mobile robots considers six dimensions for the robot pose, namely, the x, y, and z coordinates and the roll, yaw, and pitch angles. Robot motion and localization on natural surfaces, e.g., driving outdoor with a mobile robot, must regard these degrees of freedom. This paper presents a robotic mapping method based on locally consistent 3D laser range scans. Iterative Closest Point scan matching, combined with a heuristic for closed loop detection and a global relaxation method, results in a highly precise mapping system. A new strategy for fast data association, cached kd‐tree search, leads to feasible computing times. With no ground‐truth data available for outdoor environments, point relations in maps are compared to numerical relations in uncalibrated aerial images in order to assess the metric validity of the resulting 3D maps. © 2007 Wiley Periodicals, Inc.

2D vs. 3D Deformable Face Models: Representational Power, Construction, and Real-Time Fitting

Model-based face analysis is a general paradigm with applications that include face recognition, expression recognition, lip-reading, head pose estimation, and gaze estimation. A face model is first constructed from a collection of training data, either 2D images or 3D range scans. The face model is then fit to the input image(s) and the model parameters used in whatever the application is. Most existing face models can be classified as either 2D (e.g. Active Appearance Models) or 3D (e.g. Morphable Models). In this paper we compare 2D and 3D face models along three axes: (1) representational power, (2) construction, and (3) real-time fitting. For each axis in turn, we outline the differences that result from using a 2D or a 3D face model.

Fusion of laser and visual data for robot motion planning and collision avoidance

In this paper, a method for inferring scene structure information based on both laser and visual data is proposed. Common laser scanners employed in contemporary robotic systems provide accurate range measurements, but only in 2D slices of the environment. On the other hand, vision is capable of providing dense 3D information of the environment. The proposed fusion scheme combines the accuracy of laser sensors with the broad visual fields of cameras toward extracting accurate scene structure information. Data fusion is achieved by validating 3D structure assumptions formed according to 2D range scans of the environment, through the exploitation of visual information. The proposed methodology is applied to robot motion planning and collision avoidance tasks by using a suitably modified version of the vector field histogram algorithm. Experimental results confirm the effectiveness of the proposed methodology.

From range scans to 3D models

Each year, we see a growing number of 3D range scanning products on the SIGGRAPH exhibition floor. You may find yourself asking "how do these technologies work?" and "how can I make use of the shape data they produce?" In this article, I will describe a few of the more common range scanning technologies. Then, I will step through a pipeline that takes the range data into a single geometric model and will conclude with a discussion of the future of range scanning.

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 possi...