Super-resolution of Point Set Surfaces using Local Similarities

International audience3D scanners provide a virtual representation of object surfaces at some given precision that depends on many factors such as the object material, the quality of the laser-ray or the resolution of the camera. This precision may even vary over the surface, depending for example on the distance to the scanner which results in uneven and unstructured point sets, with an uncertainty on the coordinates. To enhance the quality of the scanner output, one usually resorts to local surface interpolation between measured points. However, object surfaces often exhibit interesting statistical features such as repetitive geometric textures. Building on this property, we propose a new approach for surface super-resolution that detects repetitive patterns or self-similarities and exploits them to improve the scan resolution by aggregating scattered measures. In contrast with other surface super-resolution methods, our algorithm has two important advantages. First, when handling multiple scans, it does not rely on surface registration. Second, it is able to produce super-resolution from even a single scan. These features are made possible by a new local shape description able to capture differential properties of order above 2. By comparing those descriptors, similarities are detected and used to generate a high-resolution surface. Our results show a clear resolution gain over state-of-the-art interpolation methods

Three-dimensional measurements with a novel technique combination of confocal and focus variation with a simultaneous scan

The most common optical measurement technologies used today for the three dimensional measurement of technical surfaces are Coherence Scanning Interferometry (CSI), Imaging Confocal Microscopy (IC), and Focus Variation (FV). Each one has its benefits and its drawbacks. FV will be the ideal technology for the measurement of those regions where the slopes are high and where the surface is very rough, while CSI and IC will provide better results for smoother and flatter surface regions. In this work we investigated the benefits and drawbacks of combining Interferometry, Confocal and focus variation to get better measurement of technical surfaces. We investigated a way of using Microdisplay Scanning type of Confocal Microscope to acquire on a simultaneous scan confocal and focus Variation information to reconstruct a three dimensional measurement. Several methods are presented to fuse the optical sectioning properties of both techniques as well as the topographical information. This work shows the benefit of this combination technique on several industrial samples where neither confocal nor focus variation is able to provide optimal results.Postprint (author's final draft

Sparsity Invariant CNNs

In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data. First, we show that traditional convolutional networks perform poorly when applied to sparse data even when the location of missing data is provided to the network. To overcome this problem, we propose a simple yet effective sparse convolution layer which explicitly considers the location of missing data during the convolution operation. We demonstrate the benefits of the proposed network architecture in synthetic and real experiments with respect to various baseline approaches. Compared to dense baselines, the proposed sparse convolution network generalizes well to novel datasets and is invariant to the level of sparsity in the data. For our evaluation, we derive a novel dataset from the KITTI benchmark, comprising 93k depth annotated RGB images. Our dataset allows for training and evaluating depth upsampling and depth prediction techniques in challenging real-world settings and will be made available upon publication

Assessment of the Accuracy of a Multi-Beam LED Scanner Sensor for Measuring Olive Canopies

MDPI. CC BYCanopy characterization has become important when trying to optimize any kind of agricultural operation in high-growing crops, such as olive. Many sensors and techniques have reported satisfactory results in these approaches and in this work a 2D laser scanner was explored for measuring canopy trees in real-time conditions. The sensor was tested in both laboratory and field conditions to check its accuracy, its cone width, and its ability to characterize olive canopies in situ. The sensor was mounted on a mast and tested in laboratory conditions to check: (i) its accuracy at different measurement distances; (ii) its measurement cone width with different reflectivity targets; and (iii) the influence of the target’s density on its accuracy. The field tests involved both isolated and hedgerow orchards, in which the measurements were taken manually and with the sensor. The canopy volume was estimated with a methodology consisting of revolving or extruding the canopy contour. The sensor showed high accuracy in the laboratory test, except for the measurements performed at 1.0 m distance, with 60 mm error (6%). Otherwise, error remained below 20 mm (1% relative error). The cone width depended on the target reflectivity. The accuracy decreased with the target density

Imaging Pulsed Laser Deposition oxide growth by in-situ Atomic Force Microscopy

To visualize the topography of thin oxide films during growth, thereby enabling to study its growth behavior quasi real-time, we have designed and integrated an atomic force microscope (AFM) in a pulsed laser deposition (PLD) vacuum setup. The AFM scanner and PLD target are integrated in a single support frame, combined with a fast sample transfer method, such that in-situ microscopy can be utilized after subsequent deposition pulses. The in-situ microscope can be operated from room temperature (RT) up to 700$^\circ$C and at (process) pressures ranging from the vacuum base pressure of 10$^{-6}$ mbar up to 1 mbar, typical PLD conditions for the growth of oxide films. The performance of this instrument is demonstrated by resolving unit cell height surface steps and surface topography under typical oxide PLD growth conditions.Comment: 8 pages, 8 figure