BlenSor: Blender Sensor Simulation Toolbox

Gschwandtner, Michael; Kwitt, Roland; Uhl, Andreas; Pree, Wolfgang

doi:10.1007/978-3-642-24031-7_20

Cited by 214 publications

(137 citation statements)

References 5 publications

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“…BlenSor is an add-on for Blender which allows the user to simulate various types of range scanners and other optical instruments [22]. It enables an efficient intersection of mathematical vectors (rays) with 3D objects defined in Blender and returns the range and the incidence angle.…”

Section: Simulated Laser Scanningmentioning

confidence: 99%

Terrestrial Laser Scanning for Forest Inventories—Tree Diameter Distribution and Scanner Location Impact on Occlusion

Abegg

Kükenbrink

Zell

et al. 2017

Forests

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Abstract:The rapid development of portable terrestrial laser scanning (TLS) devices in recent years has led to increased attention to their applicability for forest inventories, especially where direct measurements are very expensive or nearly impossible. However, in terms of precision and reproducibility, there are still some pending questions. In this study, we investigate the influence of stand parameters on the TLS-related visibility in forest plots. We derived 2740 stand parameters from Swiss national forest inventory sample plots. Based on these parameters, we defined virtual scenes of the forest plots with the software "Blender". Using Blender's ray-tracing features, we assessed the 3D coverage in a cubic space and 2D visibility properties for each of the virtual plots with different scanner placement schemes. We provide a formula to calculate the maximum number of possible hits for any object size at any distance from a scanner with any resolution. Additionally, we show that the Weibull scale parameter describing a stand, in addition to the number of trees and the mean diameter of the dominant 100 trees per hectare, has a significant and relevant influence on the visibility of the sample plot. Furthermore, we show the effectiveness and the efficiency of 40 scanner location patterns. These experiments demonstrate that intuitively distributing scanner locations evenly within the sample plot, with similar distances between locations and from the edge of the sample plot, provides the best overall visibility of the stand.

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Section: Simulated Laser Scanningmentioning

confidence: 99%

Terrestrial Laser Scanning for Forest Inventories—Tree Diameter Distribution and Scanner Location Impact on Occlusion

Abegg

Kükenbrink

Zell

et al. 2017

Forests

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“…The first set of reconstruction experiments were performed upon a synthetic scene, where the acquisition of the 3D point cloud has been simulated using Blender (Gschwandtner et al, 2011), which renders a synthetic geometric model as if it is viewed by a depth camera. Figure 4 shows the synthetic point cloud and the corresponding reconstruction results.…”

Section: Resultsmentioning

confidence: 99%

Implicit Surface Modeling from Imprecise Point Clouds

Funk

Dooley

Boerner

et al. 2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In applying optical methods for automated 3D indoor modelling, the 3D reconstruction of objects and surfaces is very sensitive to both lighting conditions and the observed surface properties, which ultimately compromise the utility of the acquired 3D point clouds. This paper presents a robust scene reconstruction method which is predicated upon the observation that most objects contain only a small set of primitives. The approach combines sparse approximation techniques from the compressive sensing domain with surface rendering approaches from computer graphics. The amalgamation of these techniques allows a scene to be represented by a small set of geometric primitives and to generate perceptually appealing results. The resulting scene surface models are defined as implicit functions and may be processed using conventional rendering algorithms such as marching cubes, to deliver polygonal models of arbitrary resolution. It will also be shown that 3D point clouds with outliers, strong noise and varying sampling density can be reliably processed without manual intervention.

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“…This is one of the reasons that the scan acquired by our PR2 is not as complete as the manual scan in Figure 15. Incorporating more advanced sensor models [Gschwandtner et al 2011] and searching in 6D pose space may further enhance the results using the proposed framework.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Quality-driven poisson-guided autoscanning

Wu¹,

Sun²,

Long³

et al. 2014

ACM Trans. Graph.

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Figure 1: Our robot-based, Poisson-guided autoscanner can progressively, adaptively, and fully automatically generate complete, high quality, and high fidelity scan models. AbstractWe present a quality-driven, Poisson-guided autonomous scanning method. Unlike previous scan planning techniques, we do not aim to minimize the number of scans needed to cover the object's surface, but rather to ensure the high quality scanning of the model. This goal is achieved by placing the scanner at strategically selected Next-Best-Views (NBVs) to ensure progressively capturing the geometric details of the object, until both completeness and high fidelity are reached. The technique is based on the analysis of a Poisson field and its geometric relation with an input scan. We generate a confidence map that reflects the quality/fidelity of the estimated Poisson iso-surface. The confidence map guides the generation of a viewing vector field, which is then used for computing a set of NBVs. We applied the algorithm on two different robotic platforms, a PR2 mobile robot and a one-arm industry robot. We demonstrated the advantages of our method through a number of autonomous high quality scannings of complex physical objects, as well as performance comparisons against state-of-the-art methods.

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BlenSor: Blender Sensor Simulation Toolbox

Cited by 214 publications

References 5 publications

Terrestrial Laser Scanning for Forest Inventories—Tree Diameter Distribution and Scanner Location Impact on Occlusion

Terrestrial Laser Scanning for Forest Inventories—Tree Diameter Distribution and Scanner Location Impact on Occlusion

Implicit Surface Modeling from Imprecise Point Clouds

Quality-driven poisson-guided autoscanning

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