6,936 research outputs found
Combining Stereo Disparity and Optical Flow for Basic Scene Flow
Scene flow is a description of real world motion in 3D that contains more
information than optical flow. Because of its complexity there exists no
applicable variant for real-time scene flow estimation in an automotive or
commercial vehicle context that is sufficiently robust and accurate. Therefore,
many applications estimate the 2D optical flow instead. In this paper, we
examine the combination of top-performing state-of-the-art optical flow and
stereo disparity algorithms in order to achieve a basic scene flow. On the
public KITTI Scene Flow Benchmark we demonstrate the reasonable accuracy of the
combination approach and show its speed in computation.Comment: Commercial Vehicle Technology Symposium (CVTS), 201
Anytime Stereo Image Depth Estimation on Mobile Devices
Many applications of stereo depth estimation in robotics require the
generation of accurate disparity maps in real time under significant
computational constraints. Current state-of-the-art algorithms force a choice
between either generating accurate mappings at a slow pace, or quickly
generating inaccurate ones, and additionally these methods typically require
far too many parameters to be usable on power- or memory-constrained devices.
Motivated by these shortcomings, we propose a novel approach for disparity
prediction in the anytime setting. In contrast to prior work, our end-to-end
learned approach can trade off computation and accuracy at inference time.
Depth estimation is performed in stages, during which the model can be queried
at any time to output its current best estimate. Our final model can process
1242375 resolution images within a range of 10-35 FPS on an NVIDIA
Jetson TX2 module with only marginal increases in error -- using two orders of
magnitude fewer parameters than the most competitive baseline. The source code
is available at https://github.com/mileyan/AnyNet .Comment: Accepted by ICRA201
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An evaluation framework for stereo-based driver assistance
This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury
database. However, equivalent data for automotive or robotics applications
rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while
circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases.
Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on
pixel and object level. In more detail we evaluate an intermediate representation
called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
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