9,991 research outputs found
An intelligent real time 3D vision system for robotic welding tasks
MARWIN is a top-level robot control system that has been designed for automatic robot welding tasks. It extracts welding parameters and calculates robot trajectories directly from CAD models which are then verified by real-time 3D scanning and registration. MARWIN's 3D computer vision provides a user-centred robot environment in which a task is specified by the user by simply confirming and/or adjusting suggested parameters and welding sequences. The focus of this paper is on describing a mathematical formulation for fast 3D reconstruction using structured light together with the mechanical design and testing of the 3D vision system and show how such technologies can be exploited in robot welding tasks
Fast capture of textured full-body avatar with RGB-D cameras
We present a practical system which can provide
a textured full-body avatar within three seconds. It uses sixteen
RGB-depth (RGB-D) cameras, ten of which are arranged
to capture the body, while six target the important
head region. The configuration of the multiple cameras is
formulated as a constraint-based minimum set space-covering
problem, which is approximately solved by a heuristic algorithm.
The camera layout determined can cover the fullbody
surface of an adult, with geometric errors of less than
5 mm. After arranging the cameras, they are calibrated using
a mannequin before scanning real humans. The 16 RGB-D
images are all captured within 1 s, which both avoids the
need for the subject to attempt to remain still for an uncomfortable
period, and helps to keep pose changes between different
cameras small. All scans are combined and processed
to reconstruct the photo-realistic textured mesh in 2 s. During
both system calibration and working capture of a real
subject, the high-quality RGB information is exploited to assist
geometric reconstruction and texture stitching optimization
Light Sheet Tomography (LST) for <i>in situ</i> imaging of plant roots
The production of crops capable of efficient nutrient use is essential for addressing the problem of global food security. The ability of a plant's root system to interact with the soil micro-environment determines how effectively it can extract water and nutrients. In order to assess this ability and develop the fast and cost effective phenotyping techniques which are needed to establish efficient root systems, in situ imaging in soil is required. To date this has not been possible due to the high density of scatterers and absorbers in soil or because other growth substrates do not sufficiently model the heterogeneity of a soil's microenvironment. We present here a new form of light sheet imaging with novel transparent soil containing refractive index matched particles. This imaging method does not rely on fluorescence, but relies solely on scattering from root material. We term this form of imaging Light Sheet Tomography (LST). We have tested LST on a range of materials and plant roots in transparent soil and gel. Due to the low density of root structures, i.e. relatively large spaces between adjacent roots, long-term monitoring of lettuce root development in situ with subsequent quantitative analysis was achieved
A Streaming Multi-GPU Implementation of Image Simulation Algorithms for Scanning Transmission Electron Microscopy
Simulation of atomic resolution image formation in scanning transmission
electron microscopy can require significant computation times using traditional
methods. A recently developed method, termed plane-wave reciprocal-space
interpolated scattering matrix (PRISM), demonstrates potential for significant
acceleration of such simulations with negligible loss of accuracy. Here we
present a software package called Prismatic for parallelized simulation of
image formation in scanning transmission electron microscopy (STEM) using both
the PRISM and multislice methods. By distributing the workload between multiple
CUDA-enabled GPUs and multicore processors, accelerations as high as 1000x for
PRISM and 30x for multislice are achieved relative to traditional multislice
implementations using a single 4-GPU machine. We demonstrate a potentially
important application of Prismatic, using it to compute images for atomic
electron tomography at sufficient speeds to include in the reconstruction
pipeline. Prismatic is freely available both as an open-source CUDA/C++ package
with a graphical user interface and as a Python package, PyPrismatic
Learning from Millions of 3D Scans for Large-scale 3D Face Recognition
Deep networks trained on millions of facial images are believed to be closely
approaching human-level performance in face recognition. However, open world
face recognition still remains a challenge. Although, 3D face recognition has
an inherent edge over its 2D counterpart, it has not benefited from the recent
developments in deep learning due to the unavailability of large training as
well as large test datasets. Recognition accuracies have already saturated on
existing 3D face datasets due to their small gallery sizes. Unlike 2D
photographs, 3D facial scans cannot be sourced from the web causing a
bottleneck in the development of deep 3D face recognition networks and
datasets. In this backdrop, we propose a method for generating a large corpus
of labeled 3D face identities and their multiple instances for training and a
protocol for merging the most challenging existing 3D datasets for testing. We
also propose the first deep CNN model designed specifically for 3D face
recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our
test dataset comprises 1,853 identities with a single 3D scan in the gallery
and another 31K scans as probes, which is several orders of magnitude larger
than existing ones. Without fine tuning on this dataset, our network already
outperforms state of the art face recognition by over 10%. We fine tune our
network on the gallery set to perform end-to-end large scale 3D face
recognition which further improves accuracy. Finally, we show the efficacy of
our method for the open world face recognition problem.Comment: 11 page
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