1,926 research outputs found
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
table
Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices
For many applications in low-power real-time robotics, stereo cameras are the
sensors of choice for depth perception as they are typically cheaper and more
versatile than their active counterparts. Their biggest drawback, however, is
that they do not directly sense depth maps; instead, these must be estimated
through data-intensive processes. Therefore, appropriate algorithm selection
plays an important role in achieving the desired performance characteristics.
Motivated by applications in space and mobile robotics, we implement and
evaluate a FPGA-accelerated adaptation of the ELAS algorithm. Despite offering
one of the best trade-offs between efficiency and accuracy, ELAS has only been
shown to run at 1.5-3 fps on a high-end CPU. Our system preserves all
intriguing properties of the original algorithm, such as the slanted plane
priors, but can achieve a frame rate of 47fps whilst consuming under 4W of
power. Unlike previous FPGA based designs, we take advantage of both components
on the CPU/FPGA System-on-Chip to showcase the strategy necessary to accelerate
more complex and computationally diverse algorithms for such low power,
real-time systems.Comment: 8 pages, 7 figures, 2 table
Hydra: An Accelerator for Real-Time Edge-Aware Permeability Filtering in 65nm CMOS
Many modern video processing pipelines rely on edge-aware (EA) filtering
methods. However, recent high-quality methods are challenging to run in
real-time on embedded hardware due to their computational load. To this end, we
propose an area-efficient and real-time capable hardware implementation of a
high quality EA method. In particular, we focus on the recently proposed
permeability filter (PF) that delivers promising quality and performance in the
domains of HDR tone mapping, disparity and optical flow estimation. We present
an efficient hardware accelerator that implements a tiled variant of the PF
with low on-chip memory requirements and a significantly reduced external
memory bandwidth (6.4x w.r.t. the non-tiled PF). The design has been taped out
in 65 nm CMOS technology, is able to filter 720p grayscale video at 24.8 Hz and
achieves a high compute density of 6.7 GFLOPS/mm2 (12x higher than embedded
GPUs when scaled to the same technology node). The low area and bandwidth
requirements make the accelerator highly suitable for integration into SoCs
where silicon area budget is constrained and external memory is typically a
heavily contended resource
3D high definition video coding on a GPU-based heterogeneous system
H.264/MVC is a standard for supporting the sensation of 3D, based on coding from 2 (stereo) to N views. H.264/MVC adopts many coding options inherited from single view H.264/AVC, and thus its complexity is even higher, mainly because the number of processing views is higher. In this manuscript, we aim at an efficient parallelization of the most computationally intensive video encoding module for stereo sequences. In particular, inter prediction and its collaborative execution on a heterogeneous platform. The proposal is based on an efficient dynamic load balancing algorithm and on breaking encoding dependencies. Experimental results demonstrate the proposed algorithm's ability to reduce the encoding time for different stereo high definition sequences. Speed-up values of up to 90× were obtained when compared with the reference encoder on the same platform. Moreover, the proposed algorithm also provides a more energy-efficient approach and hence requires less energy than the sequential reference algorith
Memory-efficient belief propagation for high-definition real-time stereo matching systems
Tele-presence systems will enable participants to feel like they are physically together. In order to improve this feeling, these systems are starting to include depth estimation capabilities. A typical requirement for these systems includes high definition, good quality results and low latency. Benchmarks demonstrate that stereo-matching algorithms using Belief Propagation (BP) produce the best results. The execution time of the BP algorithm in a CPU cannot satisfy real-time requirements with high-definition images. GPU-based implementations of BP algorithms are only able to work in real-time with small-medium size images because the traffic with memory limits their applicability. The inherent parallelism of the BP algorithm makes FPGA-based solutions a good choice. However, even though the memory traffic of a commercial FPGA-based ASIC-prototyping board is high, it is still not enough to comply with realtime, high definition and good immersive feeling requirements. The work presented estimates depth maps in less than 40 milliseconds for high-definition images at 30fps with 80 disparity levels. The proposed double BP topology and the new data-cost estimation improve the overall classical BP performance while they reduce the memory traffic by about 21%. Moreover, the adaptive message compression method and message distribution in memory reduce the number of memory accesses by more than 70% with an almost negligible loss of performance. The total memory traffic reduction is about 90%, demonstrating sufficient quality to be classified within the first 40 positions in the Middlebury ranking.This work has been partially supported by the CDTI under project CENIT-VISION 2007-1007 and the
CICYT under TEC2008-04107
Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras
Color-depth cameras (RGB-D cameras) have become the primary sensors in most
robotics systems, from service robotics to industrial robotics applications.
Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and
extrinsic calibration that generally does not meet the accuracy requirements
needed by many robotics applications (e.g., highly accurate 3D environment
reconstruction and mapping, high precision object recognition and localization,
...). In this paper, we propose a human-friendly, reliable and accurate
calibration framework that enables to easily estimate both the intrinsic and
extrinsic parameters of a general color-depth sensor couple. Our approach is
based on a novel two components error model. This model unifies the error
sources of RGB-D pairs based on different technologies, such as
structured-light 3D cameras and time-of-flight cameras. Our method provides
some important advantages compared to other state-of-the-art systems: it is
general (i.e., well suited for different types of sensors), based on an easy
and stable calibration protocol, provides a greater calibration accuracy, and
has been implemented within the ROS robotics framework. We report detailed
experimental validations and performance comparisons to support our statements
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