6,124 research outputs found
Acceleration of stereo-matching on multi-core CPU and GPU
This paper presents an accelerated version of a
dense stereo-correspondence algorithm for two different parallelism
enabled architectures, multi-core CPU and GPU. The
algorithm is part of the vision system developed for a binocular
robot-head in the context of the CloPeMa 1 research project.
This research project focuses on the conception of a new clothes
folding robot with real-time and high resolution requirements
for the vision system. The performance analysis shows that
the parallelised stereo-matching algorithm has been significantly
accelerated, maintaining 12x and 176x speed-up respectively
for multi-core CPU and GPU, compared with non-SIMD singlethread
CPU. To analyse the origin of the speed-up and gain
deeper understanding about the choice of the optimal hardware,
the algorithm was broken into key sub-tasks and the performance
was tested for four different hardware architectures
Guided Filtering based Pyramidal Stereo Matching for Unrectified Images
Stereo matching deals with recovering quantitative
depth information from a set of input images, based on the visual
disparity between corresponding points. Generally most of the
algorithms assume that the processed images are rectified. As
robotics becomes popular, conducting stereo matching in the
context of cloth manipulation, such as obtaining the disparity
map of the garments from the two cameras of the cloth folding
robot, is useful and challenging. This is resulted from the fact of
the high efficiency, accuracy and low memory requirement under
the usage of high resolution images in order to capture the details
(e.g. cloth wrinkles) for the given application (e.g. cloth folding).
Meanwhile, the images can be unrectified. Therefore, we propose
to adapt guided filtering algorithm into the pyramidical stereo
matching framework that works directly for unrectified images.
To evaluate the proposed unrectified stereo matching in terms of
accuracy, we present three datasets that are suited to especially
the characteristics of the task of cloth manipulations. By com-
paring the proposed algorithm with two baseline algorithms on
those three datasets, we demonstrate that our proposed approach
is accurate, efficient and requires low memory. This also shows
that rather than relying on image rectification, directly applying
stereo matching through the unrectified images can be also quite
effective and meanwhile efficien
Structured Light-Based 3D Reconstruction System for Plants.
Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance
RSGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems
Stereo depth estimation is used for many computer vision applications. Though
many popular methods strive solely for depth quality, for real-time mobile
applications (e.g. prosthetic glasses or micro-UAVs), speed and power
efficiency are equally, if not more, important. Many real-world systems rely on
Semi-Global Matching (SGM) to achieve a good accuracy vs. speed balance, but
power efficiency is hard to achieve with conventional hardware, making the use
of embedded devices such as FPGAs attractive for low-power applications.
However, the full SGM algorithm is ill-suited to deployment on FPGAs, and so
most FPGA variants of it are partial, at the expense of accuracy. In a non-FPGA
context, the accuracy of SGM has been improved by More Global Matching (MGM),
which also helps tackle the streaking artifacts that afflict SGM. In this
paper, we propose a novel, resource-efficient method that is inspired by MGM's
techniques for improving depth quality, but which can be implemented to run in
real time on a low-power FPGA. Through evaluation on multiple datasets (KITTI
and Middlebury), we show that in comparison to other real-time capable stereo
approaches, we can achieve a state-of-the-art balance between accuracy, power
efficiency and speed, making our approach highly desirable for use in real-time
systems with limited power.Comment: Accepted in FPT 2018 as Oral presentation, 8 pages, 6 figures, 4
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