912 research outputs found
The Fluorescence Detector of the Pierre Auger Observatory
The Pierre Auger Observatory is a hybrid detector for ultra-high energy
cosmic rays. It combines a surface array to measure secondary particles at
ground level together with a fluorescence detector to measure the development
of air showers in the atmosphere above the array. The fluorescence detector
comprises 24 large telescopes specialized for measuring the nitrogen
fluorescence caused by charged particles of cosmic ray air showers. In this
paper we describe the components of the fluorescence detector including its
optical system, the design of the camera, the electronics, and the systems for
relative and absolute calibration. We also discuss the operation and the
monitoring of the detector. Finally, we evaluate the detector performance and
precision of shower reconstructions.Comment: 53 pages. Submitted to Nuclear Instruments and Methods in Physics
Research Section
SurfelMeshing: Online Surfel-Based Mesh Reconstruction
We address the problem of mesh reconstruction from live RGB-D video, assuming
a calibrated camera and poses provided externally (e.g., by a SLAM system). In
contrast to most existing approaches, we do not fuse depth measurements in a
volume but in a dense surfel cloud. We asynchronously (re)triangulate the
smoothed surfels to reconstruct a surface mesh. This novel approach enables to
maintain a dense surface representation of the scene during SLAM which can
quickly adapt to loop closures. This is possible by deforming the surfel cloud
and asynchronously remeshing the surface where necessary. The surfel-based
representation also naturally supports strongly varying scan resolution. In
particular, it reconstructs colors at the input camera's resolution. Moreover,
in contrast to many volumetric approaches, ours can reconstruct thin objects
since objects do not need to enclose a volume. We demonstrate our approach in a
number of experiments, showing that it produces reconstructions that are
competitive with the state-of-the-art, and we discuss its advantages and
limitations. The algorithm (excluding loop closure functionality) is available
as open source at https://github.com/puzzlepaint/surfelmeshing .Comment: Version accepted to IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Variable Resolution & Dimensional Mapping For 3d Model Optimization
Three-dimensional computer models, especially geospatial architectural data sets, can be visualized in the same way humans experience the world, providing a realistic, interactive experience. Scene familiarization, architectural analysis, scientific visualization, and many other applications would benefit from finely detailed, high resolution, 3D models. Automated methods to construct these 3D models traditionally has produced data sets that are often low fidelity or inaccurate; otherwise, they are initially highly detailed, but are very labor and time intensive to construct. Such data sets are often not practical for common real-time usage and are not easily updated. This thesis proposes Variable Resolution & Dimensional Mapping (VRDM), a methodology that has been developed to address some of the limitations of existing approaches to model construction from images. Key components of VRDM are texture palettes, which enable variable and ultra-high resolution images to be easily composited; texture features, which allow image features to integrated as image or geometry, and have the ability to modify the geometric model structure to add detail. These components support a primary VRDM objective of facilitating model refinement with additional data. This can be done until the desired fidelity is achieved as practical limits of infinite detail are approached. Texture Levels, the third component, enable real-time interaction with a very detailed model, along with the flexibility of having alternate pixel data for a given area of the model and this is achieved through extra dimensions. Together these techniques have been used to construct models that can contain GBs of imagery data
Concepts for on-board satellite image registration, volume 1
The NASA-NEEDS program goals present a requirement for on-board signal processing to achieve user-compatible, information-adaptive data acquisition. One very specific area of interest is the preprocessing required to register imaging sensor data which have been distorted by anomalies in subsatellite-point position and/or attitude control. The concepts and considerations involved in using state-of-the-art positioning systems such as the Global Positioning System (GPS) in concert with state-of-the-art attitude stabilization and/or determination systems to provide the required registration accuracy are discussed with emphasis on assessing the accuracy to which a given image picture element can be located and identified, determining those algorithms required to augment the registration procedure and evaluating the technology impact on performing these procedures on-board the satellite
MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures
Neural Radiance Fields (NeRFs) have demonstrated amazing ability to
synthesize images of 3D scenes from novel views. However, they rely upon
specialized volumetric rendering algorithms based on ray marching that are
mismatched to the capabilities of widely deployed graphics hardware. This paper
introduces a new NeRF representation based on textured polygons that can
synthesize novel images efficiently with standard rendering pipelines. The NeRF
is represented as a set of polygons with textures representing binary opacities
and feature vectors. Traditional rendering of the polygons with a z-buffer
yields an image with features at every pixel, which are interpreted by a small,
view-dependent MLP running in a fragment shader to produce a final pixel color.
This approach enables NeRFs to be rendered with the traditional polygon
rasterization pipeline, which provides massive pixel-level parallelism,
achieving interactive frame rates on a wide range of compute platforms,
including mobile phones.Comment: CVPR 2023. Project page: https://mobile-nerf.github.io, code:
https://github.com/google-research/jax3d/tree/main/jax3d/projects/mobilener
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