An out-of-core method for GPU image mapping on large 3D scenarios of the real world
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2022-03
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Image mapping on 3D huge scenarios of the real world is one of the most fundamental and
computational expensive processes for the integration of multi-source sensing data. Recent studies
focused on the observation and characterization of Earth have been enhanced by the proliferation
of Unmanned Aerial Vehicle (UAV) and sensors able to capture massive datasets with a high spatial
resolution. Despite the advances in manufacturing new cameras and versatile platforms, only a few
methods have been developed to characterize the study area by fusing heterogeneous data such as
thermal, multispectral or hyperspectral images with high-resolution 3D models. The main reason for
this lack of solutions is the challenge to integrate multi-scale datasets and high computational efforts
required for image mapping on dense and complex geometric models. In this paper, we propose
an efficient pipeline for multi-source image mapping on huge 3D scenarios. Our GPU-based solution
significantly reduces the run time and allows us to generate enriched 3D models on-site. The proposed
method is out-of-core and it uses available resources of the GPU’s machine to perform two main
tasks: (i) image mapping and (ii) occlusion testing. We deploy highly-optimized GPU-kernels for image
mapping and detection of self-hidden geometry in the 3D model, as well as a GPU-based parallelization
to manage the 3D model considering several spatial partitions according to the GPU capabilities. Our
method has been tested on 3D scenarios with different point cloud densities (66M, 271M, 542M) and
two sets of multispectral images collected by two drone flights. We focus on launching the proposed
method on three platforms: (i) System on a Chip (SoC), (ii) a user-grade laptop and (iii) a PC. The
results demonstrate the method’s capabilities in terms of performance and versatility to be computed
by commodity hardware. Thus, taking advantage of GPUs, this method opens the door for embedded
and edge computing devices for 3D image mapping on large-scale scenarios in near real-time
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Parallel computing, GPGPU, Image mapping, 3D model