Noise analysis and synthesis for 3D laser depth scanners

This paper analyses the noise present in range data measured by a Konica Minolta Vivid 910 scanner, in order to better characterise real scanner noise. Methods for denoising 3D mesh data have often assumed the noise to be Gaussian, and independently distributed at each mesh point. We show via measurements of an accurately machined almost planar test surface that real scanner data does not have such properties: the errors are not quite Gaussian, and more importantly, exhibit significant short range correlation. We use this to give a simple model for generating noise with similar characteristics. We also consider how noise varies with such factors as laser intensity, orientation of the surface, and distance from the scanner. Finally, we evaluate the performance of three typical mesh denoising algorithms using real and synthetic test data, and suggest that new denoising algorithms are required for effective removal of real noise

Learning sparse representations of depth

This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, typically obtained by laser range scanners or structured light depth cameras. Sparse representations are learned from the Middlebury database disparity maps and then exploited in a two-layer graphical model for inferring depth from stereo, by including a sparsity prior on the learned features. Since they capture higher-order dependencies in the depth structure, these priors can complement smoothness priors commonly used in depth inference based on Markov Random Field (MRF) models. Inference on the proposed graph is achieved using an alternating iterative optimization technique, where the first layer is solved using an existing MRF-based stereo matching algorithm, then held fixed as the second layer is solved using the proposed non-stationary sparse coding algorithm. This leads to a general method for improving solutions of state of the art MRF-based depth estimation algorithms. Our experimental results first show that depth inference using learned representations leads to state of the art denoising of depth maps obtained from laser range scanners and a time of flight camera. Furthermore, we show that adding sparse priors improves the results of two depth estimation methods: the classical graph cut algorithm by Boykov et al. and the more recent algorithm of Woodford et al.Comment: 12 page

Classification and information structure of the Terrestrial Laser Scanner: methodology for analyzing the registered data of Vila Vella, historic center of Tossa de Mar

This paper presents a methodology for an architectural survey, based on the Terrestrial Laser Scanning technology TLS, not as a simple measurement and representation work, but with the purpose understanding the projects being studied, starting from the analysis, as a process of distinction and separation of the parts of a whole, in order to know their principles or elements. As a case study we start from the Vila Vella recording, conducted by the City’s Virtual Modeling Laboratory in 2008, being taken up from the start, in relation to the registration, georeferencing, filtering and handling. Aimed at a later stage of decomposition and composition of data, in terms of floor plan and facades, using semiautomatic classification techniques, for the detection of vegetation as well as the relationship of the planes of the surfaces, leading to reorganize the information from 3D data to 2D and 2.5D, considering information management, as well as the characteristics of the case study presented, in the development of methods for the construction and exploitation of new databases, to be exploited by the Geographic Information Systems and Remote Sensing.Peer Reviewe

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

Classification and information structure of the Terrestrial Laser Scanner: methodology for analyzing the registered data of Vila Vella, historic center of Tossa de Mar

This paper presents a methodology for an architectural survey, based on the Terrestrial Laser Scanning technology TLS, not as a simple measurement and representation work, but with the purpose understanding the projects being studied, starting from the analysis, as a process of distinction and separation of the parts of a whole, in order to know their principles or elements. As a case study we start from the Vila Vella recording, conducted by the City’s Virtual Modeling Laboratory in 2008, being taken up from the start, in relation to the registration, georeferencing, filtering and handling. Aimed at a later stage of decomposition and composition of data, in terms of floor plan and facades, using semiautomatic classification techniques, for the detection of vegetation as well as the relationship of the planes of the surfaces, leading to reorganize the information from 3D data to 2D and 2.5D, considering information management, as well as the characteristics of the case study presented, in the development of methods for the construction and exploitation of new databases, to be exploited by the Geographic Information Systems and Remote Sensing.Peer Reviewe

Automated 3D model generation for urban environments [online]

Abstract In this thesis, we present a fast approach to automated generation of textured 3D city models with both high details at ground level and complete coverage for birds-eye view. A ground-based facade model is acquired by driving a vehicle equipped with two 2D laser scanners and a digital camera under normal traffic conditions on public roads. One scanner is mounted horizontally and is used to determine the approximate component of relative motion along the movement of the acquisition vehicle via scan matching; the obtained relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques using an aerial photograph or a Digital Surface Model as a global map. The second scanner is mounted vertically and is used to capture the 3D shape of the building facades. Applying a series of automated processing steps, a texture-mapped 3D facade model is reconstructed from the vertical laser scans and the camera images. In order to obtain an airborne model containing the roof and terrain shape complementary to the facade model, a Digital Surface Model is created from airborne laser scans, then triangulated, and finally texturemapped with aerial imagery. Finally, the facade model and the airborne model are fused to one single model usable for both walk- and fly-thrus. The developed algorithms are evaluated on a large data set acquired in downtown Berkeley, and the results are shown and discussed