Fast Semantic Segmentation of RGB-D Scenes with GPU-Accelerated Deep Neural Networks

Abstract. In semantic scene segmentation, every pixel of an image is assigned a category label. This task can be made easier by incorporat-ing depth information, which structured light sensors provide. Depth, however, has very different properties from RGB image channels. In this paper, we present a novel method to provide depth information to convo-lutional neural networks. For this purpose, we apply a simplified version of the histogram of oriented depth (HOD) descriptor to the depth chan-nel. We evaluate the network on the challenging NYU Depth V2 dataset and show that with our method, we can reach competitive performance at a high frame rate

3D Object Recognition Based on Volumetric Representation Using Convolutional Neural Networks

Following the success of Convolutional Neural Networks on object recognition and image classification using 2D images; in this work the framework has been extended to process 3D data. However, many current systems require huge amount of computation cost for dealing with large amount of data. In this work, we introduce an efficient 3D volumetric representation for training and testing CNNs and we also build several datasets based on the volumetric representation of 3D digits, different rotations along the x, y and z axis are also taken into account. Unlike the normal volumetric representation, our datasets are much less memory usage. Finally, we introduce a model based on the combination of CNN models, the structure of the model is based on the classical LeNet. The accuracy result achieved is beyond the state of art and it can classify a 3D digit in around 9 ms

Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting

We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a vector of descriptors for each sample. In the next step, an iterative merging algorithm recombines the output of the over-segmentation into larger regions matching the various elements of the scene. The couples of adjacent segments with higher similarity according to the CNN features are candidate to be merged and the surface fitting accuracy is used to detect which couples of segments belong to the same surface. Finally, a set of labelled segments is obtained by combining the segmentation output with the descriptors from the CNN. Experimental results show how the proposed approach outperforms state-of-the-art methods and provides an accurate segmentation and labelling

Depth-aware convolutional neural networks for accurate 3D pose estimation in RGB-D images

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Most recent approaches to 3D pose estimation from RGB-D images address the problem in a two-stage pipeline. First, they learn a classifier –typically a random forest– to predict the position of each input pixel on the object surface. These estimates are then used to define an energy function that is minimized w.r.t. the object pose. In this paper, we focus on the first stage of the problem and propose a novel classifier based on a depth-aware Convolutional Neural Network. This classifier is able to learn a scale-adaptive regression model that yields very accurate pixel-level predictions, allowing to finally estimate the pose using a simple RANSAC-based scheme, with no need to optimize complex ad hoc energy functions. Our experiments on publicly available datasets show that our approach achieves remarkable improvements over state-of-the-art methods.Peer ReviewedPostprint (author's final draft

INDOOR SEMANTIC SEGMENTATION FROM RGB-D IMAGES BY INTEGRATING FULLY CONVOLUTIONAL NETWORK WITH HIGHER-ORDER MARKOV RANDOM FIELD

Indoor scenes have the characteristics of abundant semantic categories, illumination changes, occlusions and overlaps among objects, which poses great challenges for indoor semantic segmentation. Therefore, we in this paper develop a method based on higher-order Markov random field model for indoor semantic segmentation from RGB-D images. Instead of directly using RGB-D images, we first train and perform RefineNet model only using RGB information for generating the high-level semantic information. Then, the spatial location relationship from depth channel and the spectral information from color channels are integrated as a prior for a marker-controlled watershed algorithm to obtain the robust and accurate visual homogenous regions. Finally, higher-order Markov random field model encodes the short-range context among the adjacent pixels and the long-range context within each visual homogenous region for refining the semantic segmentations. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on the public SUN RGB-D dataset. Experimental results indicate that compared with using RGB information alone, the proposed method remarkably improves the semantic segmentation results, especially at object boundaries

Artificial Intelligence Applied to Supply Chain Management and Logistics: Systematic Literature Review

The growing impact of automation and artificial intelligence (AI) on supply chain management and logistics is remarkable. This technological advance has the potential to significantly transform the handling and transport of goods. The implementation of these technologies has boosted efficiency, predictive capabilities and the simplification of operations. However, it has also raised critical questions about AI-based decision-making. To this end, a systematic literature review was carried out, offering a comprehensive view of this phenomenon, with a specific focus on management. The aim is to provide insights that can guide future research and decision-making in the logistics and supply chain management sectors. Both the articles in this thesis and that form chapters present detailed methodologies and transparent results, reinforcing the credibility of the research for researchers and managers. This contributes to a deeper understanding of the impact of technology on logistics and supply chain management. This research offers valuable information for both academics and professionals in the logistics sector, revealing innovative solutions and strategies made possible by automation. However, continuous development requires vigilance, adaptation, foresight and a rapid problem-solving capacity. This research not only sheds light on the current panorama, but also offers a glimpse into the future of logistics in a world where artificial intelligence is set to prevail

Deep Learning based 3D Segmentation: A Survey

3D object segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving, robotics, augmented reality and medical image analysis. It has received significant attention from the computer vision, graphics and machine learning communities. Traditionally, 3D segmentation was performed with hand-crafted features and engineered methods which failed to achieve acceptable accuracy and could not generalize to large-scale data. Driven by their great success in 2D computer vision, deep learning techniques have recently become the tool of choice for 3D segmentation tasks as well. This has led to an influx of a large number of methods in the literature that have been evaluated on different benchmark datasets. This paper provides a comprehensive survey of recent progress in deep learning based 3D segmentation covering over 150 papers. It summarizes the most commonly used pipelines, discusses their highlights and shortcomings, and analyzes the competitive results of these segmentation methods. Based on the analysis, it also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure

Segmentation of color and depth data based on surface fitting

This thesis presents novel iterative schemes for the segmentation of scenes acquired by RGB-D sensors. Both the problems of objects segmentation and of semantic segmentation (labeling) are considered. The first building block of the proposed methods is the Normalized Cuts algorithm, based on graph theory and spectral clustering techniques, that provides a segmentation exploiting both geometry and color information. A limitation is the fact that the number of segments (equivalently, the number of objects in the scene) must either be decided in advance, or requires an arbitrary threshold on the normalized cut measure to be controlled. In addition, this method tends to provide segments of similar size, while in many real world scenes the dimensions of the objects and structures are widely variable. To overcome these drawbacks, we present iterative schemes based on the approximation with parametric NURBS surfaces (Non-Uniform Rational B-Splines). The key idea is to consider the results of the surface fitting as an estimation of how good the current segmentation is. This makes it possible to build region splitting and region merging procedures, in which the fitting results are compared at each step against the previous ones, and the iterations are moved forward based on whether they turn out to be improved or not, until an optimal final solution is reached. The rationale is that, if a segment properly corresponds to an actual object in the scene, the fitting result is expected to be good, while segments that need to be subdivided or merged with other ones are expected to give a larger error. A discussion of several possible metrics to evaluate the quality of the surface fitting is presented. In all the presented schemes, the employment of NURBS surfaces approximation is a novel contribution. Subsequently, it is described how the proposed iterative schemes can be coupled with a Deep Learning classification step performed with CNNs (Convolutional Neural Networks), by introducing a measure of similarity between the elements of an initial over-segmentation. This information is used together with the surface fitting results to control the steps of a revised iterative region merging procedure. In addition, some information (fitting error, surface curvatures) resulting from the NURBS fitting on the initial over-segmentation is fed into the Convolutional Neural Networks themselves. To the best of our knowledge, this is the first work where this kind of information is used within a Deep Learning framework. Finally, the objects segmentation resulting from the region merging procedure is exploited to effectively improve the initial classification. An extensive evaluation of the proposed methods is performed, with quantitative comparison against several state-of-the-art approaches on a standard dataset. The experimental results show that the proposed schemes provide equivalent or better results with respect to the competing approaches on most of the considered scenes, both for the task of objects segmentation and for the task of semantic labeling. In particular, the optimal number of segments is automatically provided by the iterative procedures, while it must be arbitrarily set in advance on several other segmentation algorithms. Moreover, no assumption is done on the objects shape, while some competing methods are optimized for planar surfaces. This is provided by the usage of NURBS surfaces as geometric model, since they can represent both simple entities as planes, spheres, cylinders, and complex free-form shapes

Deep learning for scene understanding with color and depth data

Significant advancements have been made in the recent years concerning both data acquisition and processing hardware, as well as optimization and machine learning techniques. On one hand, the introduction of depth sensors in the consumer market has made possible the acquisition of 3D data at a very low cost, allowing to overcome many of the limitations and ambiguities that typically affect computer vision applications based on color information. At the same time, computationally faster GPUs have allowed researchers to perform time-consuming experimentations even on big data. On the other hand, the development of effective machine learning algorithms, including deep learning techniques, has given a highly performing tool to exploit the enormous amount of data nowadays at hand. Under the light of such encouraging premises, three classical computer vision problems have been selected and novel approaches for their solution have been proposed in this work that both leverage the output of a deep Convolutional Neural Network (ConvNet) as well jointly exploit color and depth data to achieve competing results. In particular, a novel semantic segmentation scheme for color and depth data is presented that uses the features extracted from a ConvNet together with geometric cues. A method for 3D shape classification is also proposed that uses a deep ConvNet fed with specific 3D data representations. Finally, a ConvNet for ToF and stereo confidence estimation has been employed underneath a ToF-stereo fusion algorithm thus avoiding to rely on complex yet inaccurate noise models for the confidence estimation task