Concept: 3D scanner
Population surveys and species recognition for roosting bats are either based on capture, sight or optical-mechanical count methods. However, these methods are intrusive, are tedious and, at best, provide only statistical estimations. Here, we demonstrated the successful use of a terrestrial Light Detection and Ranging (LIDAR) laser scanner for remotely identifying and determining the exact population of roosting bats in caves. LIDAR accurately captured the 3D features of the roosting bats and their spatial distribution patterns in minimal light. The high-resolution model of the cave enabled an exact count of the visibly differentiated Hipposideros larvatus and their roosting pattern within the 3D topology of the cave. We anticipate that the development of LIDAR will open up new research possibilities by allowing researchers to study roosting behaviour within the topographical context of a cave’s internal surface, thus facilitating rigorous quantitative characterisations of cave roosting behaviour.
Laser scanning technology is one of the most integral parts of today’s scientific research, manufacturing, defense, and biomedicine. In many applications, high-speed scanning capability is essential for scanning a large area in a short time and multi-dimensional sensing of moving objects and dynamical processes with fine temporal resolution. Unfortunately, conventional laser scanners are often too slow, resulting in limited precision and utility. Here we present a new type of laser scanner that offers ∼1,000 times higher scan rates than conventional state-of-the-art scanners. This method employs spatial dispersion of temporally stretched broadband optical pulses onto the target, enabling inertia-free laser scans at unprecedented scan rates of nearly 100 MHz at 800 nm. To show our scanner’s broad utility, we use it to demonstrate unique and previously difficult-to-achieve capabilities in imaging, surface vibrometry, and flow cytometry at a record 2D raster scan rate of more than 100 kHz with 27,000 resolvable points.
Brain source localization accuracy in magnetoencephalography (MEG) requires accuracy in both digitizing anatomical landmarks and coregistering to anatomical magnetic resonance images (MRI). We compared the source localization accuracy and MEG-MRI coregistration accuracy of two head digitization systems-a laser scanner and the current standard electromagnetic digitization system (Polhemus)-using a calibrated phantom and human data. When compared using the calibrated phantom, surface and source localization accuracy for data acquired with the laser scanner improved over the Polhemus by 141% and 132%, respectively. Laser scan digitization reduced MEG source localization error by 1.38 mm on average. In human participants, a laser scan of the face generated a 1000-fold more points per unit time than the Polhemus head digitization. An automated surface-matching algorithm improved the accuracy of MEG-MRI coregistration over the equivalent manual procedure. Simulations showed that the laser scan coverage could be reduced to an area around the eyes only while maintaining coregistration accuracy, suggesting that acquisition time can be substantially reduced. Our results show that the laser scanner can both reduce setup time and improve localization accuracy, in comparison to the Polhemus digitization system.
The goal of this study was to develop a more accurate formula to forecast tooth-size discrepancies in patients based on not only the size of the whole teeth but also functional arch components derived from normal cusp-fossa interdigitation that should be obtained as the final treatment goal.
Terrestrial laser scanning is of increasing importance for surveying and hazard assessments. Digital terrain models are generated using the resultant data to analyze surface processes. In order to determine the terrain surface as precisely as possible, it is often necessary to filter out points that do not represent the terrain surface. Examples are vegetation, vehicles, and animals. Filtering in mountainous terrain is more difficult than in other topography types. Here, existing automatic filtering solutions are not acceptable, because they are usually designed for airborne scan data. The present article describes a method specifically suitable for filtering terrestrial laser scanning data. This method is based on the direct line of sight between the scanner and the measured point and the assumption that no other surface point can be located in the area above this connection line. This assumption is only true for terrestrial laser data, but not for airborne data. We present a comparison of the wedge filtering to a modified inverse distance filtering method (IDWMO) filtered point cloud data. Both methods use manually filtered surfaces as reference. The comparison shows that the mean error and root-mean-square-error (RSME) between the results and the manually filtered surface of the two methods are similar. A significantly higher number of points of the terrain surface could be preserved, however, using the wedge-filtering approach. Therefore, we suggest that wedge-filtering should be integrated as a further parameter into already existing filtering processes, but is not suited as a standalone solution so far.
Detailed up-to-date ground reference data have become increasingly important in quantitative forest inventories. Field reference data are conventionally collected at the sample plot level by means of manual measurements, which are both labor-intensive and time-consuming. In addition, the number of attributes collected from the tree stem is limited. More recently, terrestrial laser scanning (TLS), using both single-scan and multi-scan techniques, has proven to be a promising solution for efficient stem mapping at the plot level. In the single-scan method, the laser scanner is placed at the center of the plot, creating only one scan, and all trees are mapped from the single-scan point cloud. Consequently, the occlusion of stems increases as the range of the scanner increases, depending on the forest’s attributes. In the conventional multi-scan method, several scans are made simultaneously inside and outside of the plot to collect point clouds representing all trees within the plot, and these scans are accurately co-registered by using artificial reference targets manually placed throughout the plot. The additional difficulty of applying the multi-scan method is due to the point-cloud registration of several scans not being fully automated yet. This paper proposes a multi-single-scan (MSS) method to map the sample plot. The method does not require artificial reference targets placed on the plot or point-level registration. The MSS method is based on the fully automated processing of each scan independently and on the merging of the stem positions automatically detected from multiple scans to accurately map the sample plot. The proposed MSS method was tested on five dense forest plots. The results show that the MSS method significantly improves the stem-detection accuracy compared with the single-scan approach and achieves a mapping accuracy similar to that achieved with the multi-scan method, without the need for the point-level registration.
The point of origin of an impact pattern is important in establishing the chain of events in a bloodletting incident. In this study, the accuracy and reproducibility of the point of origin estimation using the FARO Scene software with the FARO Focus(3D) laser scanner was determined. Five impact patterns were created for each of three combinations of distances from the floor (z) and the front wall (x). Fifteen spatters were created using a custom impact rig, scanned using the laser scanner, photographed using a DSLR camera, and processed using the Scene software. Overall results gave a SD = 3.49 cm (p < 0.0001) in the x-direction, SD = 1.14 cm (p = 0.9291) in the y-direction, and SD = 9.08 cm (p < 0.0115) in the z-direction. The technique performs within literature ranges of accepted accuracy and reproducibility and is comparable to results reported for other virtual stringing software.
Soft-tissue deformations can severely degrade the validity of preoperative planning data during computer assisted interventions. Intraoperative imaging such as stereo endoscopic, time-of-flight or, laser range scanner data can be used to compensate these movements. In this context, the intraoperative surface has to be matched to the preoperative model. The shape matching is especially challenging in the intraoperative setting due to noisy sensor data, only partially visible surfaces, ambiguous shape descriptors, and real-time requirements.
The Dual-Wavelength Echidna Lidar (DWEL), a full waveform terrestrial laser scanner (TLS), has been used to scan a variety of forested and agricultural environments. From these scanning campaigns, we summarize the benefits and challenges given by DWEL’s novel coaxial dual-wavelength scanning technology, particularly for the three-dimensional (3D) classification of vegetation elements. Simultaneous scanning at both 1064 nm and 1548 nm by DWEL instruments provides a new spectral dimension to TLS data that joins the 3D spatial dimension of lidar as an information source. Our point cloud classification algorithm explores the utilization of both spectral and spatial attributes of individual points from DWEL scans and highlights the strengths and weaknesses of each attribute domain. The spectral and spatial attributes for vegetation element classification each perform better in different parts of vegetation (canopy interior, fine branches, coarse trunks, etc.) and under different vegetation conditions (dead or live, leaf-on or leaf-off, water content, etc.). These environmental characteristics of vegetation, convolved with the lidar instrument specifications and lidar data quality, result in the actual capabilities of spectral and spatial attributes to classify vegetation elements in 3D space. The spectral and spatial information domains thus complement each other in the classification process. The joint use of both not only enhances the classification accuracy but also reduces its variance across the multiple vegetation types we have examined, highlighting the value of the DWEL as a new source of 3D spectral information. Wider deployment of the DWEL instruments is in practice currently held back by challenges in instrument development and the demands of data processing required by coaxial dual- or multi-wavelength scanning. But the simultaneous 3D acquisition of both spectral and spatial features, offered by new multispectral scanning instruments such as the DWEL, opens doors to study biophysical and biochemical properties of forested and agricultural ecosystems at more detailed scales.
Sensing advances in plant phenotyping are of vital importance in basic and applied plant research. Plant phenotyping enables the modeling of complex shapes, which is useful, for example, in decision-making for agronomic management. In this sense, 3D processing algorithms for plant modeling is expanding rapidly with the emergence of new sensors and techniques designed to morphologically characterize. However, there are still some technical aspects to be improved, such as an accurate reconstruction of end-details. This study adapted low-cost techniques, Structure from Motion (SfM) and MultiView Stereo (MVS), to create 3D models for reconstructing plants of three weed species with contrasting shape and plant structures. Plant reconstruction was developed by applying SfM algorithms to an input set of digital images acquired sequentially following a track that was concentric and equidistant with respect to the plant axis and using three different angles, from a perpendicular to top view, which guaranteed the necessary overlap between images to obtain high precision 3D models. With this information, a dense point cloud was created using MVS, from which a 3D polygon mesh representing every plants' shape and geometry was generated. These 3D models were validated with ground truth values (e.g., plant height, leaf area (LA) and plant dry biomass) using regression methods. The results showed, in general, a good consistency in the correlation equations between the estimated values in the models and the actual values measured in the weed plants. Indeed, 3D modeling using SfM algorithms proved to be a valuable methodology for weed phenotyping, since it accurately estimated the actual values of plant height and LA. Additionally, image processing using the SfM method was relatively fast. Consequently, our results indicate the potential of this budget system for plant reconstruction at high detail, which may be usable in several scenarios, including outdoor conditions. Future research should address other issues, such as the time-cost relationship and the need for detail in the different approaches.