nils-herrmann/clean_scirepeval huggingface dataset:
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes

We present a 3D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin image representation. The spin image is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spin images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes.
Large data sets and confusing scenes in 3-D surface matching and recognition

We report on recent extensions to a surface matching algorithm based on local 3D signatures. This algorithm was previously shown to be effective in view registration of general surfaces and in object recognition from 3D model databases. We describe extensions to the basic matching algorithm which will enable it to address several challenging and often overlooked problems encountered with real data. First, we describe extensions that allow us to deal with data sets with large variations in resolution and with large data sets for which computational efficiency is a major issue. The applicability of the enhanced matching algorithm is illustrated by an example application: the construction of large terrain maps and the construction of accurate 3D models from unregistered views. Second, we describe extensions that facilitate the use of 3D object recognition in cases in which the scene contains a large amount of clutter (e.g., the object occupies 1% of the scene) and in which the scene presents a high degree of confusion (e.g., the model shape is close to other shapes in the scene). Those last two extensions involve learning recognition strategies from the description of the model and from the performance of the recognition algorithm using Bayesian and memory based learning techniques, respectively.
Unconstrained registration of large 3D point sets for complex model building

We present a method for building models of complex environments from range data gathered at multiple viewpoints. Our approach is unique in that no prior knowledge of the relative positions of the viewpoints is needed in order to register data from them. Furthermore, we present a technique for specification and utilization of so-called "common-sense" constraints on the transformations between views to improve the accuracy and speed of the registration process. Results are shown from our effort to map a 60 m by 20 m multiple-room storage area containing a cluttered array of objects.
Active laser radar for high-performance measurements

Laser scanners, or laser radars (ladar), have been used for a number of years for mobile robot navigation and inspection tasks. Although previous scanners were sufficient for low speed applications, they often did not have the range or angular resolution necessary for mapping at the long distances. Many also did not provide an ample field of view with high accuracy and high precision. In this paper we will present the development of state-of-the-art, high speed, high accuracy, 3D laser radar technology. This work has been a joint effort between CMU and K2T and Z+F. The scanner mechanism provides an unobstructed 360/spl deg/ horizontal field of view, and a 70/spl deg/ vertical field of view. Resolution of the scanner is variable with a maximum resolution of approximately 0.06 degrees per pixel in both azimuth and elevation. The laser is amplitude-modulated, continuous-wave with an ambiguity interval of 52 m, a range resolution of 1.6 mm, and a maximum pixel rate of 625 kHz. This paper will focus on the design and performance of the laser radar and will discuss several potential applications for the technology. It reports on performance data of the system including noise, drift over time, precision, and accuracy with measurements. Influences of ambient light, surface material of the target and ambient temperature for range accuracy are discussed. Example data of applications will be shown and improvements will also be discussed.
Toward a General 3-D Matching Engine: Multiple Models, Complex Scenes, and Efficient Data Filtering

We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor that is used to match surfaces represented as surface meshes. Starting with the general matching framework introduced earlier, we present a compression scheme for spin-images; this scheme results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. In addition, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes. We address efficiency and generality through two extensions to the basic matching scheme: fast filtering of scene points and processing of general data sets.
Control of Polygonal Mesh Resolution for 3-D Computer Vision

A common representation in 3-D computer vision is the polygonal surface mesh because meshes can model objects of arbitrary shape and are easily constructed from sensed 3-D data. The resolution of a surface mesh is the overall spacing between vertices that comprise the mesh. Because sensed 3-D points are often unevenly distributed, the resolution of a surface mesh is often poorly defined. We present an algorithm that transforms a mesh with an uneven spacing between vertices into a mesh with a more even spacing between vertices, thus improving its definition of resolution. In addition, we show how the algorithm can be used to control the resolution of surface meshes, making them amenable to multiresolution approaches in computer vision. The structure of our algorithm is modeled on iterative mesh simplification algorithms common in computer graphics; however, the individual steps in our algorithm are designed specifically to control mesh resolution. An even spacing between vertices is generated by applying a sequence of local edge operations that promote uniform edge lengths while preserving mesh shape. To account for polyhedral objects, we introduce an accurate shape change measure that permits edge operations along sharp creases. By locally bounding the total change in mesh shape, drastic changes in global shape are prevented. We show results from many 3-D sensing domains including computed tomography, range imaging, and digital elevation map construction.
Mobility planning for autonomous navigation of multiple robots in unstructured environments

We describe an approach to mobility planning for autonomous navigation of multiple vehicles in unstructured environments. At the lowest level, this approach uses command arbitration in order to combine command recommendation from driving behaviors. Driving behaviors include local behaviors such as obstacle avoidance or goal tracking, and strategic behavior such as route planning to distant goals. Obstacle avoidance is performed by evaluating a fixed command set with respect to a terrain map built from 3D sensors. Route planning is based on the D* dynamic route planner which evaluates the command set based on the currently optimal path to the next goal. An additional planner, called GRAMMPS, is used to continuously analyze the outputs of the route planners running on each of the vehicles and re-computes optimally the allocation of goals to vehicles in order to minimize time and distance travelled. This approach has be exercised extensively with two vehicles using stereo and LADAR for obstacle detection and map building.
High-performance laser range scanner

Laser scanners, or ladars, have been used for a number of years for mobile robot navigation. Although previous scanners were sufficient for low-speed navigation, they often did not have the range or angular resolution necessary for mapping at the long distances required by high-speed navigation. Many also did not provide an ample field of view. In this paper we will present the development of state-of-the-art, high speed, high accuracy, laser range scanner technology. This work has been a joint effort between CMU and K2T in Pittsburgh and Zoller + Friehlich in Wangen, Germany. The scanner mechanism provides an unobstructed 360 degrees horizontal field of view, and a 30 degree vertical field of view. Resolution of the scanner is variable with a maximum resolution of approximately 0.06 degrees per pixel in both azimuth and elevation. The laser is amplitude-modulated, continuous-wave with an ambiguity interval of 52 metes, a range resolution of 1.6 mm, and a maximum pixel rate of 500 kHz. This paper will focus on the design and performance of the scanner mechanism and will discuss several potential applications for the technology. One application, obstacle detection for automated highway applications will be discussed in more detail. Example data will be shown and current mechanism improvements from the CMU prototype will also be discussed.
Efficient multiple model recognition in cluttered 3-D scenes

We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spin-images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes.
Evaluation of Image-Based Landmark Recognition Techniques

Recognizing landmarks in sequences of images is a challenging problem for a number of reasons. First of all, the appearance of any given landmark varies substantially from one observation to the next. In addition to variations due to different aspects, an illumination change, external clutter, and changing geometry of the imaging devices are other factors affecting the variability of the observed landmarks. Finally, it is typically difficult to make use of accurate 3D information in landmark recognition applications. For those reasons, it is not possible to use many of the object recognition techniques based on strong geometric models. The alternative is to use image-based techniques in which landmarks are represented by collections of images which capture the “typical” appearance of the object. The information most relevant to recognition is extracted from the collection of raw images and used as the model for recognition. This process is often referred to as “visual learning.” Models of landmarks are acquired from image sequences and later recognized for vehicle localization in urban environments. In the acquisition phase, a vehicle drives and collects images of an unknown area. The algorithm organizes these images into groups with similar image features. The feature distribution for each group describes a landmark. In the recognition phase, while navigating through the same general area, the vehicle collects new images. The algorithm classifies these images into one of the learned groups, thus recognizing a landmark. Unlike computationally intensive model-based approaches that build models from known objects observed in isolation, our image-based approach automatically learns the most salient landmarks in complex environments. It delivers a robust performance under a wide range of lighting and imaging angle variations.
Spectro-polarimetric imager for intelligent transportation systems

We have built a portable spectro-polarimetric machine vision system that operates at video frame rates. Our system contains only electronically controllable components, including an imaging acousto-optic tunable filter (AOTF), a phase retarder, acceptance and imaging optics, and a standard CCD-based camera. The device operates like an ordinary camera, except that a computer controls the spectral and polarization content of light to be viewed. For example, by sweeping the wavelength over the AOTF's range, one can obtain a spectral signature for each pixel in an image. Alternately, the camera can switch between two wavelengths, allowing for high-speed discrimination of closely matched colors in a scene. In addition to digitally controlling the wavelength, our imager uses a liquid crystal retarder to filter images based on polarization signatures of objects. We have implemented a number of algorithms to take advantage of the unique capabilities of our sensor, some of which can be applied to problems specific to transportation systems. We present two image processing applications that highlight the different methods we use to analyze scenes with our system. One application uses spectral processing to locate vegetation in a scene; the second uses polarization signatures to detect glare from hazardous road conditions such as water and ice.
On Generating Multi-resolution Representations of Polygonal Meshes

Multi-resolution representation is one of the effective approaches to image processing in both computer vision and computer graphics. However, the generation of multi-resolution representation for complex 3D polygonal mesh is a difficult problem. In this paper, a number of tools developed in our research group for this purpose are described. These tools employ different techniques such as smoothing, edge length normalization and wavelets to create representations which describe 3D shape at multi-resolutions. Unlike the control of mesh resolution known as mesh simplification or mesh decimation in computer graphics, the tools we have developed are in the context of computer vision, in particular, object recognition and surface matching. Application examples are given for each of the representations generated by the above tools. Comparison of the representations is also discussed.
Experiments in autonomous driving with concurrent goals and multiple vehicles

In this paper we report on experiments with a system for autonomously driving two vehicles based on complex mission specifications. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e.g., the ordering of the goals, based on the updated paths to the goals. Two recently developed sensors are used for obstacle detection: a high-speed laser rangefinder and a video-rate stereo system. An updated version of a dynamic path planner D* is used for online computation of routes. A new mission planning and execution monitoring tool, GRAMMPS, is used for managing the allocation and ordering of goals between vehicles. We report on experiments conducted in an outdoor test site with two HMMWVs. Implementation details and performance analysis, including failure modes, are described based on a series of twelve experiments, each over 1/2 km distance with up to nine goals. This system is the first multivehicle and multigoal system to be demonstrated in real, natural environments with this degree of generality. The work reported here includes a number of results not previously published, including the use of a real-time stereo machine, a high performance laser rangefinder and the GRAMMPS planning system.
Surface Matching for Object Recognition in Complex 3-D Scenes

We present an approach to recognition of complex objects in cluttered 3-D scenes that does not require feature extraction or segmentation. Our object representation comprises descriptive images associated with oriented points on the surface of an object. Using a single point basis constructed from an oriented point, the position of other points on the surface of the object can be described by two parameters. The accumulation of these parameters for many points on the surface of the object results in an image at each oriented point. These images, localized descriptions of the global shape of the object, are invariant to rigid transformations. Through correlation of images, point correspondences between a model and scene data are established. Geometric consistency is used to group the correspondences from which plausible rigid transformations that align the model with the scene are calculated. The transformations are then refined and verified using a modified iterative closest point algorithm. The effectiveness of our representation comes from its ability to combine the descriptive nature of global object properties with the robustness to partial views and clutter of local shape descriptions.The wide applicability of our algorithm is demonstrated with results showing recognition of complex objects in cluttered scenes with occlusion.
Omni-directional visual servoing for human-robot interaction

We describe a visual servoing system developed as a human-robot interface to drive a mobile robot toward any chosen target. An omni-directional camera is used to get the 360 degree of field of view, and an efficient tracking technique is developed to track the target. The use of the omni-directional geometry eliminates, many of the problems common in visual tracking and makes the use of visual servoing a practical alternative for robot-human interaction. The experiments demonstrate that it is an effective and robust way to guide a robot. In particular the experiments show robustness of the tracker to loss of template, vehicle motion, and change in scale and orientation.
Map Building and Terrain-Aided Localisation in an Underground Mine

This paper discusses strategies for estimation of the location of a Load-Haul-Dump truck (LHD) in and underground mine. The results reported are based on data obtained from field trials in an underground mine in Queensland. An LHD was fitted with two two-dimensional laser rangefinders, an INS, sensors to measure vehicle speed and the articulation angle and a rotating laser scanner. An algorithm based on an extended Kaiman filter provides a poly-line map of the mine walls. Once the map is built, the iterative closest point (ICP) algorithm is used to obtain an estimate of the location of the LHD using successive scans from the laser rangefinder.
The space-time map applied to Drosophila embryogenesis

Many physical phenomena have complex structure in both space and time. To systematically understand these phenomena from images one needs representations that unify the treatment of space and time. The authors create such a representation, the space-time map, for characterizing contour evolutions. Many types of information are computed and stored as facets of the map, registered to a space-time manifold generated by the evolution. The authors demonstrate their representation on the example of Drosophila embryogenesis in optical section. Changes in embryo shape are reflected as changes in the dye distribution along the deforming vitelline membrane contour. The authors extract a series of contours and create a two-dimensional space-time map. They track intensity on this map to obtain a velocity field. They extract space-time ridges and significant motions on this map, and use them along with prior knowledge to recognize the significant features and events of embryogenesis.
Examining 4.2 Analysis

the scatter plots in Figure 6, one notices that recognition rate is effected by occlusion. At low occlusion values, no recognition failures are reported, while at high occlusion values, recognition failures dominate. This indicates that recognition will almost always work if sufficient model surface area is visible. The decrease in recognition success after a fixed level of occlusion is reached (70%) indicates that spin-image matching does not work well when only a small portion of the model is visible. This is no surprise since spin-image descriptiveness comes from accumulation of surface area around a point. In the middle of Figure 6 are shown the experimental recognition rates versus scene occlusion. The rates are computed using a gaussian weighted running average to avoid the problems with binning. These plots show that recognition rate remains high for both forms of compression until occlusion of around 70% is reached, then the successful recognition rate begins to fall off. Examining the experiment scatter plots in Figure 6, one notices that the effect of clutter on recognition is uniform across all levels of occlusion until a high level of clutter is reached. This indicates that spin-image matching is independent of the clutter in the scene. On the right in Figure 6, plots of recognition rate versus amount of clutter also show that recognition rate is fairly independent of clutter. As clutter increases, there are slight variations about a fixed recognition rate. Most likely, these variations are due to non-uniform sampling of recognition runs and are not actual trends with respect to clutter. Above a high level of clutter, the successful recognitions decline, but from the scatter plots we see that at high levels of clutter, the number of experiments is small, so conclusions about recognition rate can not be made. In all of the plots showing the effect of clutter and occlusion, the true-positive rates are higher for recognition with spin-images without compression when compared with the true-positive rates for recognition with compression. This validates the expected decrease in the accuracy of spin-image matching when using compressed spin-images. However, it should be noted that the recognition rate for both matching algorithms remain high. For all levels of clutter and occlusion, matching without compression has an average recognition rate of 90.0% and matching with compression has an average recognition rate of 83.2%. Furthermore, the false-positives rate for both algorithms are low and nearly the same. Our …
ity Planning for Auto ous Navigation Multiple Robots in Unstructured

In this papel; we describe an approach to mobility planning for autonomous navigation of multiple vehicles unstructured environments. This approach is suitable for complex missions in which multiple goals with sequencing constraints may be specified. At the lowest level, this approach uses command arbitration in order to combine command recommendation from driving behaviors. Driving behaviors include local behaviors such as obstacle avoidance or goal tracking, and strategic behavior such as route planning to distant goals. Obstacle avoidance is perjormed by evaluating a jixed command set with respect to a terrain map built from 3-0 sensors. Route planning is based on the D* dynamic route planner which evaluates the command set based on the currently optimal path to the next goal. The path is updatedfiequently based on map updates from the obstacle avoidance sensors. On top of this behavior-based system, an additional planner called GRAMMPS, continuously analyzes the outputs of the route planners running on each of the vehicles and re-computes optimally the allocation of goals to vehicles in order to minimize time and distance travelled. This approach has be exercised extensively with two vehicles using stereo and ladar for obstacle detection and map building. Experiments were conducted over long distances with missions of up to nine intermediate goals in diyerent conjigurations. We report of the implementation of this system and on its perjormance.