Correspondence-free pose estimation for 3D objects from noisy depth data

Zabulis, Xenophon; Lourakis, Manolis; Koutlemanis, Panagiotis

doi:10.1007/s00371-016-1326-9

Cited by 16 publications

(4 citation statements)

References 47 publications

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“…To estimate pose from stereo data, either the actual surface or a set of 3-D features extracted from it are matched with a 3-D model of the target (see [44] and references therein). Stereo sensing is sensitive to the lack of texture and possible illumination artifacts, such as shadows and specular reflections, which are common in space environments.…”

Section: B Stereo Approachesmentioning

confidence: 99%

High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

Lentaris¹,

Maragos²,

Stratakos³

et al. 2018

Journal of Aerospace Information Systems

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Vision-based navigation has become increasingly important in a variety of space applications for enhancing autonomy and dependability. Future missions, such as active debris removal for remediating the low Earth orbit environment, will rely on novel high-performance avionics to support advanced image processing algorithms with substantial workloads. However, when designing new avionics architectures, constraints relating to the use of electronics in space present great challenges, further exacerbated by the need for significantly faster processing compared to conventional space-grade central processing units. With the long-term goal of designing highperformance embedded computers for space, in this paper, an extended study and tradeoff analysis of a diverse set of computing platforms and architectures (i.e., central processing units, multicore digital signal processors, graphics processing units, and field-programmable gate arrays) are performed with radiation-hardened and commercial offthe-shelf technology. Overall, the study involves more than 30 devices and 10 benchmarks, which are selected after exploring the algorithms and specifications required for vision-based navigation. The present analysis combines literature survey and in-house development/testing to derive a sizable consistent picture of all possible solutions. Among others, the results show that certain 28 nm system-on-chip devices perform faster than space-grade and embedded central processing units by 1-3 orders of magnitude, while consuming less than 10 W. Field-programmable gate array platforms provide the highest performance per watt ratio.

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Section: B Stereo Approachesmentioning

confidence: 99%

High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

Lentaris¹,

Maragos²,

Stratakos³

et al. 2018

Journal of Aerospace Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are a variety of pipelines that consist of a coarse detection step, such as proposing a 2D or 3D bounding box and an object category, followed by a refinement step, such as aligning a precise, deformable 3D model to images and depths. For an overview of such pipelines, see for example [10,17,25] for monocular images, [21] for stereo images, and [42,49,55] for recovering objects from depth and color images.…”

Section: Use In Object Detection Methodsmentioning

confidence: 99%

Continuous Signed Distance Functions for 3D Vision

Haugo¹,

Stahl²,

Brekke³

2017

2017 International Conference on 3D Vision (3DV)

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This paper explores the use of continuous signed distance functions as an object representation for 3D vision. Popularized in procedural computer graphics, this representation defines 3D objects as geometric primitives combined with constructive solid geometry and transformed by nonlinear deformations, scaling, rotation or translation. Unlike their discretized counterpart, that have become important in dense 3D reconstruction, the continuous distance function is not stored as a sampled volume, but as a closed mathematical expression. Through surveys and qualitative studies we argue that this representation can have several benefits for 3D vision, such as being able to describe many classes of indoor and outdoor objects at the order of hundreds of bytes per class, getting parametrized shape variations for free. As a distance function, the representation also has useful computational aspects by defining, at each point in space, the direction and distance to the nearest surface, and whether a point is inside or outside the surface.

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“…Among 3D data processing tasks, 3D object recognition has become one of the most popular researching problems in the last two decades [1,2,3,4,5,6]. The main goals of object recognition are to correctly recognize objects in scenes and accurately estimate their poses [7].…”

Section: Introdutionmentioning

confidence: 99%

Histograms of Gaussian normal distribution for 3D feature matching in cluttered scenes

Zhou

Tong

et al. 2018

Vis Comput

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3D feature descriptors provide information between corresponding models and scenes. 3D objection recognition in cluttered scenes, however, remains a largely unsolved problem. Practical applications impose several challenges which are not fully addressed by existing methods. Especially in cluttered scenes there are many feature mismatches between scenes and models.We therefore propose Histograms of Gaussian Normal Distribution (HGND) for extracting salient features on a local reference frame (LRF) that enables us to solve this problem. We propose a LRF on each local surface patches using the scatter matrix's eigenvectors. Then the HGND information of each salient point is calculated on the LRF, for which we use both the mesh and point data of the depth image. Experiments on 45 cluttered scenes of the Bologna Dataset and 50 cluttered scenes of the UWA Dataset are made to evaluate the robustness and descriptiveness of our HGND. Experiments carried out by us demonstrate that HGND obtains a more reliable matching rate than stateof-the-art approaches in cluttered situations.

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Correspondence-free pose estimation for 3D objects from noisy depth data

Cited by 16 publications

References 47 publications

High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

Continuous Signed Distance Functions for 3D Vision

Histograms of Gaussian normal distribution for 3D feature matching in cluttered scenes

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