One-Shot HDR Imaging via Stereo PFA Cameras

High Dynamic Range (HDR) imaging techniques aim to increase the range of luminance values captured from a scene. The literature counts many approaches to get HDR images out of low-range camera sensors, however most of them rely on multiple acquisitions producing ghosting effects when moving objects are present. In this paper we propose a novel HDR reconstruction method exploiting stereo Polarimetric Filter Array (PFA) cameras to simultaneously capture the scene with different polarized filters, producing intensity attenuations that can be related to the light polarization state. An additional linear polarizer is mounted in front of one of the two cameras, raising the degree of polarization of rays captured by the sensor. This leads to a larger attenuation range between channels regardless the scene lighting condition. By merging the data acquired by the two cameras, we can compute the actual light attenuation observed by a pixel at each channel and derive an equivalent exposure time, producing a HDR picture from a single polarimetric shot. The proposed technique results comparable to classic HDR approaches using multiple exposures, with the advantage of being a one-shot method

Cylinders extraction in non-oriented point clouds as a clustering problem

Finding geometric primitives in 3D point clouds is a fundamental task in many engineering applications such as robotics, autonomous-vehicles and automated industrial inspection. Among all solid shapes, cylinders are frequently found in a variety of scenes, comprising natural or man-made objects. Despite their ubiquitous presence, automated extraction and fitting can become challenging if performed ”in-the-wild”, when the number of primitives is unknown or the point cloud is noisy and not oriented. In this paper we pose the problem of extracting multiple cylinders in a scene by means of a Game-Theoretic inlier selection process exploiting the geometrical relations between pairs of axis candidates. First, we formulate the similarity between two possible cylinders considering the rigid motion aligning the two axes to the same line. This motion is represented with a unitary dual-quaternion so that the distance between two cylinders is induced by the length of the shortest geodesic path in SE(3). Then, a Game-Theoretical process exploits such similarity function to extract sets of primitives maximizing their inner mutual consensus. The outcome of the evolutionary process consists in a probability distribution over the sets of candidates (ie axes), which in turn is used to directly estimate the final cylinder parameters. An extensive experimental section shows that the proposed algorithm offers a high resilience to noise, since the process inherently discards inconsistent data. Compared to other methods, it does not need point normals and does not require a fine tuning of multiple parameters

Quantization-Aware NN Layers with High-throughput FPGA Implementation for Edge AI

Over the past few years, several applications have been extensively exploiting the advantages of deep learning, in particular when using convolutional neural networks (CNNs). The intrinsic flexibility of such models makes them widely adopted in a variety of practical applications, from medical to industrial. In this latter scenario, however, using consumer Personal Computer (PC) hardware is not always suitable for the potential harsh conditions of the working environment and the strict timing that industrial applications typically have. Therefore, the design of custom FPGA (Field Programmable Gate Array) solutions for network inference is gaining massive attention from researchers and companies as well. In this paper, we propose a family of network architectures composed of three kinds of custom layers working with integer arithmetic with a customizable precision (down to just two bits). Such layers are designed to be effectively trained on classical GPUs (Graphics Processing Units) and then synthesized to FPGA hardware for real-time inference. The idea is to provide a trainable quantization layer, called Requantizer, acting both as a non-linear activation for neurons and a value rescaler to match the desired bit precision. This way, the training is not only quantization-aware, but also capable of estimating the optimal scaling coefficients to accommodate both the non-linear nature of the activations and the constraints imposed by the limited precision. In the experimental section, we test the performance of this kind of model while working both on classical PC hardware and a case-study implementation of a signal peak detection device running on a real FPGA. We employ TensorFlow Lite for training and comparison, and use Xilinx FPGAs and Vivado for synthesis and implementation. The results show an accuracy of the quantized networks close to the floating point version, without the need for representative data for calibration as in other approaches, and performance that is better than dedicated peak detection algorithms. The FPGA implementation is able to run in real time at a rate of four gigapixels per second with moderate hardware resources, while achieving a sustained efficiency of 0.5 TOPS/W (tera operations per second per watt), in line with custom integrated hardware accelerators

A physics-driven CNN model for real-time sea waves 3D reconstruction

One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to iteratively update the wave spectrum over time. Albeit substantially faster, the unpre-dictable convergence time of the optimization involved still prevents its usage as a continuously running remote sensing infrastructure. In this work, we build upon the same idea, but investigat-ing the feasibility of a fully data-driven Machine Learning (ML) approach. We designed a novel Convolutional Neural Network that learns how to produce an accurate surface from the scattered elevation data of three subsequent frames. The key idea is to embed the linear dispersion relation into the model itself to physically relate the sparse points observed at different times. Assuming that the scattered data are uniformly distributed in the spatial domain, this has the same effect of increasing the sample density of each single frame. Experiments demonstrate how the proposed technique, even if trained with purely synthetic data, can produce accurate and physically consistent surfaces at five frames per second on a modern PC

A Relevance-Based CNN Trimming Method for Low-Resources Embedded Vision

A significant amount of Deep Learning research deals with the reduction of network complexity. In most scenarios the preservation of very high performance has priority over size reduction. However, when dealing with embedded systems, the limited amount of resources forces a switch in perspective. In fact, being able to dramatically reduce complexity could be a stronger requisite for overall feasibility than excellent performance. In this paper we propose a simple to implement yet effective method to largely reduce the size of Convolutional Neural Networks with minimal impact on their performance. The key idea is to assess the relevance of each kernel with respect to a representative dataset by computing the output of its activation function and to trim them accordingly. The resulting network becomes small enough to be adopted on embedded hardware, such as smart cameras or lightweight edge processing units. In order to assess the capability of our method with respect to real-world scenarios, we adopted it to shrink two different pre-trained networks to be hosted on general purpose low-end FPGA hardware to be found in embedded cameras. Our experiments demonstrated both the overall feasibility of the method and its superior performance when compared with similar size-reducing techniques introduced in recent literature

Robust phase unwrapping by probabilistic consensus

Structured light scanning works by projecting over the scene a supplement of controlled information: the captured signal is processed to provide a unique label (namely a code) for each observed point, and then proceed to geometrical triangulation. In phase shift profilometry sinusoidal patterns are projected and each point is labelled according to the observed phase. Then, due to the periodic nature of the signal, a disambiguation method (known as phase unwrapping) is needed. Several unwrapping techniques have been proposed in the literature, since noisy signals lead to inaccuracies in phase estimation. This paper presents a novel phase unwrapping approach based on a probabilistic framework. The method involves the projection of multiple sinusoidal patterns with distinct period lengths, encoding different phase values at each point location. Phase values are then modelled as samples from a Wrapped Gaussian distribution with an unknown mean, determined by the projector code that generated the values. This formulation allows us to robustly perform phase unwrapping via Maximum Likelihood Estimation, recovering code values from the observed phases. Furthermore, the same likelihood function can be exploited to identify and correct faulty unwrappings by gauging mutual support in a spatial neighbourhood. An extensive experimental assessment validates the Gaussian distribution hypothesis and verifies the improvements in coding accuracy when compared to other classical unwrapping techniques

Mapping a coastal transition in braided systems: an example from the Precipice Sandstone, Surat Basin

The Precipice Sandstone is traditionally interpreted as a braided fluvial deposit that transitions upwards into meandering channel deposits responding to a rise in base level that eventually deposits the overlying alluvial to lacustrine Evergreen Formation. This study found sedimentary evidence of tidal to marine influence within the Precipice Sandstone coincident with avulsion and diversion of the system from southward to northward-flowing channels as the system was transgressed. The north-flowing channels are interpreted to debouch into a shallow restricted marine embayment with tide and wave influence, which provides an alternative insight into this unit and suggests a Lower Jurassic north or northeasterly marine connection. The Precipice Sandstone is a regional aquifer, in places hosts hydrocarbons and has been considered as a storage unit for CO2 geosequestration. Outcrop analogues can provide geometries to accompany facies interpreted from sedimentary structures that are observable in core, to assist in characterising reservoir heterogeneity

Geolocating Time: Digitisation and Reverse Engineering of a Roman Sundial

The sundial of Euporus was discovered in 1878 within the ancient Roman city of Aquileia (Italy), in a quite unusual location at the centre of the city’s horse race track. Studies have tried to demonstrate that the sundial had been made for a more southern location than the one it was found at, although no specific alternative positions have been suggested. This paper showcases both the workflow designed to fully digitise it in 3D and analyses on the use of the artefact undertaken from it. The final 3D reconstruction achieves accuracies of a few millimetres, thus offering the opportunity to analyse small details of its surface and to perform non-trivial measurements. We also propose a mathematical approach to compute the object’s optimal working latitude as well as the gnomon position and orientation. The algorithm is designed as an optimization problem where the sundial’s inscriptions and the Sun positions during daytime are considered to obtain the optimal configuration. The complete 3D model of the object is used to get all the geometrical information needed to validate the results of computations