1,697 research outputs found
Data Fusion of Objects Using Techniques Such as Laser Scanning, Structured Light and Photogrammetry for Cultural Heritage Applications
In this paper we present a semi-automatic 2D-3D local registration pipeline
capable of coloring 3D models obtained from 3D scanners by using uncalibrated
images. The proposed pipeline exploits the Structure from Motion (SfM)
technique in order to reconstruct a sparse representation of the 3D object and
obtain the camera parameters from image feature matches. We then coarsely
register the reconstructed 3D model to the scanned one through the Scale
Iterative Closest Point (SICP) algorithm. SICP provides the global scale,
rotation and translation parameters, using minimal manual user intervention. In
the final processing stage, a local registration refinement algorithm optimizes
the color projection of the aligned photos on the 3D object removing the
blurring/ghosting artefacts introduced due to small inaccuracies during the
registration. The proposed pipeline is capable of handling real world cases
with a range of characteristics from objects with low level geometric features
to complex ones
Statistical Process Monitoring of Isolated and Persistent Defects in Complex Geometrical Shapes
Traditional Statistical Process Control methodologies face several challenges
when monitoring defects in complex geometries, such as those of products
obtained via Additive Manufacturing techniques. Many approaches cannot be
applied in these settings due to the high dimensionality of the data and the
lack of parametric and distributional assumptions on the object shapes.
Motivated by a case study involving the monitoring of egg-shaped trabecular
structures, we investigate two recently-proposed methodologies to detect
deviations from the nominal IC model caused by excess or lack of material. Our
study focuses on the detection of both isolated large changes in the geometric
structure, as well as persistent small deviations. We compare the approach of
Scimone et al. (2022) with Zhao and del Castillo (2021) for monitoring defects
in a small Phase I sample of 3D-printed objects. While the former control chart
is able to detect large defects, the latter allows the detection of
nonconforming objects with persistent small defects. Furthermore, we address
the fundamental issue of selecting the number of eigenvalues to be monitored in
Zhao and del Castillo's method by proposing a dimensionality reduction
technique based on kernel principal components. This approach is shown to
provide a good detection capability even when considering a large number of
eigenvalues. By leveraging the sensitivity of the two monitoring schemes to
different magnitudes of nonconformities, we also propose a novel joint
monitoring scheme that is capable of identifying both types of defects in the
considered case study. Computer code in R and Matlab that implements these
methods and replicates the results is available as part of the supplementary
material.Comment: 39 pages, 5 figures, 3 table
Zero-Shot Point Cloud Registration
Learning-based point cloud registration approaches have significantly
outperformed their traditional counterparts. However, they typically require
extensive training on specific datasets. In this paper, we propose , the first
zero-shot point cloud registration approach that eliminates the need for
training on point cloud datasets. The cornerstone of ZeroReg is the novel
transfer of image features from keypoints to the point cloud, enriched by
aggregating information from 3D geometric neighborhoods. Specifically, we
extract keypoints and features from 2D image pairs using a frozen pretrained 2D
backbone. These features are then projected in 3D, and patches are constructed
by searching for neighboring points. We integrate the geometric and visual
features of each point using our novel parameter-free geometric decoder.
Subsequently, the task of determining correspondences between point clouds is
formulated as an optimal transport problem. Extensive evaluations of ZeroReg
demonstrate its competitive performance against both traditional and
learning-based methods. On benchmarks such as 3DMatch, 3DLoMatch, and ScanNet,
ZeroReg achieves impressive Recall Ratios (RR) of over 84%, 46%, and 75%,
respectively
3D mapping of indoor and outdoor environments using apple smart devices
Recent integration of LiDAR into smartphones opens up a whole new world of possibilities for 3D indoor/outdoor mapping. Although these new systems offer an unprecedent opportunity for the democratization in the use of scanning technology, data quality is lower than data captured from high-end LiDAR sensors. This paper is focused on discussing the capability of recent Apple smart devices for applications related with 3D mapping of indoor and outdoor environments. Indoor scenes are evaluated from a reconstruction perspective, and three geometric aspects (local precision, global correctness, and surface coverage) are considered using data captured in two adjacent rooms. Outdoor environments are analysed from a mobility point of view, and elements defining the physical accessibility in building entrances are considered for evaluation.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. RYC2020-029193-
LANDSAT-D investigations in snow hydrology
Work undertaken during the contract and its results are described. Many of the results from this investigation are available in journal or conference proceedings literature - published, accepted for publication, or submitted for publication. For these the reference and the abstract are given. Those results that have not yet been submitted separately for publication are described in detail. Accomplishments during the contract period are summarized as follows: (1) analysis of the snow reflectance characteristics of the LANDSAT Thematic Mapper, including spectral suitability, dynamic range, and spectral resolution; (2) development of a variety of atmospheric models for use with LANDSAT Thematic Mapper data. These include a simple but fast two-stream approximation for inhomogeneous atmospheres over irregular surfaces, and a doubling model for calculation of the angular distribution of spectral radiance at any level in an plane-parallel atmosphere; (3) incorporation of digital elevation data into the atmospheric models and into the analysis of the satellite data; and (4) textural analysis of the spatial distribution of snow cover
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