Uwe Stilla

Technische Universität München | TUM · Chair of Photogrammetry and Remote Sensing

Monitoring building efficiency is a hot topic for engineers and researchers. TIR (Thermal Infrared) images describe thermal attributes but require professional knowledge for analysis. Moreover, images can hardly describe the thermal attributes of the buildings in all aspects. Therefore, as-built thermal point clouds become the solution. In this cas...

Reconstructing semantic 3D building models at the level of detail (LoD) 3 is a long-standing challenge. Unlike mesh-based models, they require watertight geometry and object-wise semantics at the fa\c{c}ade level. The principal challenge of such demanding semantic 3D reconstruction is reliable fa\c{c}ade-level semantic segmentation of 3D input data...

Recent works on 3D single object tracking treat the task as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for the tracking. However, it is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete. In this paper, we ad...

Point clouds are widely regarded as one of the best dataset types for urban mapping purposes. Hence, point cloud datasets are commonly investigated as benchmark types for various urban interpretation methods. Yet, few researchers have addressed the use of point cloud benchmarks for fa\c{c}ade segmentation. Robust fa\c{c}ade segmentation is becoming...

Semantic 3D building models are widely available and used in numerous applications. Such 3D building models display rich semantics but no fa\c{c}ade openings, chiefly owing to their aerial acquisition techniques. Hence, refining models' fa\c{c}ades using dense, street-level, terrestrial point clouds seems a promising strategy. In this paper, we pro...

Over recent decades, 3D point clouds have been a popular data source applied in automatic change detection in a wide variety of applications. Compared with 2D images, using 3D point clouds for change detection can provide an alternative solution offering different modalities and enabling a highly detailed 3D geometric and attribute analysis. This a...

Volltext im Tagungsband: ISBN 978-3-87907-738-0 ------- https://www.isbn.de/buch/9783879077380/22-internationale-geodaetische-woche-obergurgl-2023

3D point cloud semantic segmentation plays an essential role in fine-grained scene understanding from photogrammetry to autonomous driving. Although recent efforts have been made to push the 3D semantic segmentation forward, many solutions cannot generalize well to new data with different sensor configurations. For example, when transferring the se...

Place recognition based on point cloud (LiDAR) scans is an important module for achieving robust autonomy in robots or self-driving vehicles. Training deep networks to match such scans presents a difficult trade-off: a higher spatial resolution of the network's intermediate representations is needed to perform fine-grained matching of subtle geomet...

Visibility analysis plays a vital role in the design and placing of traffic signs in the urban street environment. This work investigates the occlusion detection of traffic lights and traffic signs caused by vegetation. The presented analysis method is built upon the inputs from the expected situation reflected by a highly detailed 3D city model an...

Semantic 3D building models are widely available and used in numerous applications. Such 3D building models display rich semantics but no façade openings, chiefly owing to their aerial acquisition techniques. Hence, refining models’ façades using dense, street-level, terrestrial point clouds seems a promising strategy. In this paper, we propose a m...

Automatic construction progress documentation and metric evaluation of execution work in confined building interiors requires particularly reliable geometric evaluation and interpretation of statistically uncertain as-built point clouds. This paper presents a method for high-resolution change detection based on dense 3D point clouds from terrestria...

Prefabricated buildings, as the development center of architectural industrialization, will produce a lot of point-cloud data in the process of information detection and management. These point-cloud data can be used for reverse modeling to restore the physical characteristics of prefabricated concrete components, which can provide a basis for pref...

In this article a novel SAR change detection (CD) method for the monitoring of man-made objects (MMO) is presented. Rather than looking for changes in SAR amplitude or the loss of coherence, changes are detected by the appearance and disappearance of the strong point scatterers present in MMO and often denoted as coherent scatterers (CSs). This ena...

Automated change detection based on urban mobile laser scanning data is the foundation for a whole range of applications such as building model updates, map generation for autonomous driving and natural disaster assessment. The challenge with mobile LiDAR data is that various sources of error, such as localization errors, lead to uncertainties and...

Semantic 3D building models are provided by public authorities and can be used in applications, such as urban planning, simulations, navigation, and many others. Since large-scale 3D models are typically derived from top-view digital surface models (DSM), they can have detailed roof structures but render planes for façade elements. Furthermore, bui...

Gravitational mass movements represent a significant hazard potential in Alpine regions. Due to climate change and an associated increases in extreme weather events, this risk is growing. For a better predictability of such events and the monitoring of affected areas, a precise determination of the ongoing movements is necessary. In this paper, a m...

Vehicle self-localization is one of the most important capabilities for automated driving. Current localization methods already provide accuracy in the centimeter range, so robustness becomes a key factor, especially in urban environments. There is no commonly used standard metric for the robustness of localization systems, but a set of different a...

Recent works on 3D single object tracking treat the tracking as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for tracking. However, it is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete. In this paper, we ad...

Point clouds are widely regarded as one of the best dataset types for urban mapping purposes. Hence, point cloud datasets are commonly investigated as benchmark types for various urban interpretation methods. Yet, few researchers have addressed the use of point cloud benchmarks for façade segmentation. Robust façade segmentation is becoming a key f...

Der moderne Automobilbau, insbesondere im Premiumsegment, setzt äußerst enge Toleranzen hinsichtlich der Oberflächengüte des Endproduktes voraus. Beginnend mit der Blechumformung im Presswerk, über den Karosseriebau, bis hin zur Lackierung und Endmontage, müssen etwaige Oberflächenfehler im teilweise einstelligen Mikrometerbereich detektiert, klass...

As the key to the construction progress monitoring, methods and strategies for change detection using 3D point clouds from various sources have been investigated for years. However, how to achieve object-level change detection with uncertainty evaluation is still an unsolved topic. Occlusions and noise in 3D points and other attribute information,...

Trees play an important role in the complex system of urban environments. Their benefits to environment and health are manifold. Yet, especially near streets, the traffic can be impaired by a limited clearance. Even injuries could be caused by breaking tree parts. Hence, it is important to capture the trees in the frame of a tree cadastre and to en...

Throughout the years, semantic 3D city models have been created to depict 3D spatial phenomenon. Recently, an increasing number of mobile laser scanning (MLS) units yield terrestrial point clouds at an unprecedented level. Both dataset types often depict the same 3D spatial phenomenon differently, thus their fusion should increase the quality of th...

Registration is a fundamental but critical task in point cloud processing, which usually depends on finding element correspondence from two point clouds. However, the finding of reliable correspondence relies on establishing a robust and discriminative description of elements and the correct matching of corresponding elements. In this letter, we de...

High-precision self-localization is one of the most important capabilities of automated vehicles. Not only accuracy but also localization robustness are crucial for self-driving vehicles in urban environments. The localization robustness decreases by misclassifications of landmarks and therefore false matches between dynamic objects and static land...

Semantic labeling is an essential but challenging task when interpreting point clouds of 3D scenes. As a core step for scene interpretation, semantic labeling is the task of annotating every point in the point cloud with a label of semantic meaning, which plays a significant role in plenty of point cloud related applications. For airborne laser sc...

This paper presents an approach which combines LiDAR sensors and cameras of a mobile multi-sensor system to obtain information about pedestrians in the vicinity of the sensor platform. Such information can be used, for example, in the context of driver assistance systems. In the first step, our approach starts by using LiDAR sensor data to detect a...

In our daily lives, trees can be seen as the tallest and most noticeable representatives of the plant kingdom. Especially in urban areas, the individual tree is of high significance and responsible for a manifold of positive effects on the environment and residents. In the context of urban tree registers and thus monitoring of urban vegetation, we...

Path planning for a measuring vehicle requires solving two popular problems from computer science, namely the search for the optimal tour and the search for the optimal viewpoint. Combining both problems results in a new variation of the Traveling Salesman Problem, which we refer to as the Explorational Traveling Salesman Problem. The solution to t...

When purchasing a premium car for a substantial sum, first impressions count. Key to that first impression is a flawless exterior appearance, something self-explanatory for the customer, but a far greater challenge for production than one might initially assume. Fortunately, photogrammetric technologies and evaluation methods are enabling an ever g...

We tackle the problem of place recognition from point cloud data and introduce a self-attention and orientation encoding network (SOE-Net) that fully explores the relationship between points and incorporates long-range context into point-wise local descriptors. Local information of each point from eight orientations is captured in a PointOE module,...

Construction progress documentation is currently of great interest for the AEC (Architecture, Engineering and Construction) branch and BIM (Building Information Modeling). Subject of this work is the geometric accuracy assessment of image-based change detection in indoor environments based on a BIM. Line features usually serve well as geodetic refe...

Nowadays, the number of connected devices providing unstructured data is rapidly rising. These devices acquire data with a temporal and spatial resolution at an unprecedented level creating an influx of geoinformation which, however, lacks semantic information. Simultaneously, structured datasets like semantic 3D city models are widely available an...

We present the current state of development of the sensor-equipped car MODISSA, with which Fraunhofer IOSB realizes a configurable experimental platform for hardware evaluation and software development in the context of mobile mapping and vehicle-related safety and protection. MODISSA is based on a van that has successively been equipped with a var...

This entry refers to the authors' version of the article that has been accepted for publication in Applied Optics, 9 May 2021. A PDF file of the authors' version is available at https://arxiv.org/abs/2105.13580. For the final edited and published version of record, see https://doi.org/10.1364/AO.423599. Please cite as "MODISSA: a multipurpose plat...

In this paper a new method is introduced for the automatic estimation of all the relevant parameters of oil storage tanks using a single high resolution SAR image. For a given storage tank, this method will estimate its maximum capacity and determine whether it has a fixed or a floating roof. For tanks with a floating roof, the amount of oil stored...

Registration is a fundamental but critical task in point cloud processing, which usually depends on finding element correspondence from two point clouds. However, the finding of reliable correspondence relies on establishing a robust and discriminative description of elements and the correct matching of corresponding elements. In this letter, we de...

We tackle the problem of object completion from point clouds and propose a novel point cloud completion network employing an asymmetrical Siamese feature matching strategy, termed as ASFM-Net. Specifically, the asymmetrical Siamese auto-encoder neural network is adopted to map the partial and complete input point cloud into a shared latent space, w...

Point clouds acquired through laser scanning and stereo vision techniques have been applied in a wide range of applications, proving to be optimal sources for mapping 3D urban scenes. Point clouds provide 3D spatial coordinates of geometric surfaces, describing the real 3D world with both geometric information and attributes. However, unlike 2D ima...

Nowadays, point clouds acquired through laser scanning and stereo matching have deemed to be one of the best sources for mapping urban scenes. Spatial coordinates of 3D points directly reflect the geometry of object surfaces, which significantly streamlining the 3D reconstruction and modeling of objects. The construction industry has utilized point...

As a dynamic and essential component in the road environment of urban scenarios, vehicles are the most popular investigation targets. To monitor their behavior and extract their geometric characteristics, an accurate and instant measurement of vehicles plays a vital role in traffic and transportation fields. Point clouds acquired from the mobile la...

Point cloud registration is invariably an essential and challenging task in the fields of photogrammetry and computer vision to align multiple point clouds to a united reference frame. In this paper, we propose a novel global registration method using a robust phase correlation method for registration of low-overlapping point clouds, which is less...

In this paper a new method is introduced for the automatic estimation of all the relevant parameters of oil storage tanks using a single high resolution SAR image. For a given storage tank, this method will estimate its maximum capacity and determine whether it has a fixed or a floating roof. For tanks with a floating roof, the amount of oil stored...

Buildings take a large proportion of the total energy consumption in the city area in winter, therefore Thermal Infrared (TIR) images are widely used to evaluate the energy consumption and leakage of the building. To overcome the difficulties of image interpretation and occlusion in TIR images, utilizing thermal information in 3D structures that fu...

In this work, we propose a novel neural network focusing on semantic labeling of ALS point clouds, which investigates the importance of long-range spatial and channel-wise relations and is termed as global relation-aware attentional network (GraNet). GraNet first learns local geometric description and local dependencies using a local spatial discre...

This article presents a framework to generate 3-D point clouds by very-high-resolution single-pass and single-channel circular synthetic aperture radar (CSAR). The focus is both on the precise 3-D determination of very small, detached objects and on larger buildings in complex urban scenes. Inspired by optical flow methods, our approach evaluates t...

We tackle the problem of place recognition from point cloud data and introduce a self-attention and orientation encoding network (SOE-Net) that fully explores the relationship between points and incorporates long-range context into point-wise local descriptors. Local information of each point from eight orientations is captured in a PointOE module,...

Plane segmentation is a simple yet essential processing step for using 3D point clouds in applications such as temporal data registration and object modeling. The performance of traditional plane segmentation algorithms, especially when addressing photogrammetric point clouds, is significantly limited by factors including the errors of the point po...

Registration of multi-temporal data is an important task when conducting construction monitoring and analysis using 3D point clouds acquired at different time points. However, due to the complexity of scenes in construction sites and the intrinsic attributes of the datasets (e.g., noise, outliers, and uneven densities), the registration of multi-te...

Image registration is a fundamental issue in photogrammetry and remote sensing, which targets to find the alignment between different images. Recently, registration of images from difference sensors become the hot topic. The registered images from different sensors are able to offer additional information, which help with different tasks like segme...

Vehicles are the most concerned investigation target as a dynamic and essential component in the road environment of urban scenarios. To monitor their behaviors and extract their geometric characteristics, an accurate and instant measurement of the vehicles plays a vital role in remote sensing and computer vision field. 3D point clouds acquired fro...

In this paper, we demonstrate the inclusion of a top-view camera system mounted on a city bus in an existing sensor setup. A novel sensor setup with five down-facing cameras is mounted on the roof of a MAN Lion’s City 12 city bus to extract landmarks in road scene images. Its positioning is validated by an exemplary detection of lane markings. The...

This paper presents and extends an approach for the detection of pedestrians in unstructured point clouds resulting from single MLS (mobile laser scanning) scans. The approach is based on a neural network and a subsequent voting process. The neural network processes point clouds subdivided into local point neighborhoods. The member points of these...

This contribution discusses the accuracy and the applicability of Photogrammetric point clouds based on dense image matching for the monitoring of gravitational mass movements caused by crevices. Four terrestrial image sequences for three different time epochs have been recorded and oriented using ground control point in a local reference frame. Fo...

Registration of multiple point clouds acquired via terrestrial laser scanning (TLS) is usually compulsory to obtain the scanned data covering a whole urban scene. However, the automated processing of aligning multiple scans is still a concern because of the complex urban environment. To this end, we propose a fast and sturdy estimation of 3D shifts...

Completing the 3D shape of vehicles from real scan data, which aims to estimate the complete geometry of vehicles from partial inputs, acts as a role in the field of remote sensing and autonomous driving. With the recent popularity of deep learning, plenty of data-driven methods have been proposed. However, most of them usually require additional i...

Registration of point clouds is a fundamental problem in the community of photogrammetry and 3D computer vision. Generally, point cloud registration consists of two steps: the search of correspondences and the estimation of transformation parameters. However, to find correspondences from point clouds, generating robust and discriminative features i...

Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We pre...

Satellite jitter is a common and complicated phenomenon that degrades the geometric quality of high-resolution satellite images. Imagery-based detection and compensation of satellite jitter have recently been widely concerned. However, most of the existing studies overlook the issue of image interpolation in this topic involving subpixel measuremen...

Automated construction-progress monitoring enables the required transparency for improved process control, and is thus being increasingly adopted by the construction industry. Many recent approaches use Scan-to/vs-BIM methods for capturing the as-built status of large construction sites. However, they often lack accuracy or are incomplete due to oc...

The semantic labeling of the urban area is an essential but challenging task for a wide variety of applications such as mapping, navigation, and monitoring. The rapid advance in Light Detection and Ranging (LiDAR) systems provides this task with a possible solution using 3D pointclouds, which are accessible, affordable, accurate, and applicable. Am...

In the past decade, a vast amount of strategies, methods, and algorithms have been developed to explore the semantic interpretation of 3D point clouds for extracting desirable information. To assess the performance of the developed algorithms or methods, public standard benchmark datasets should invariably be introduced and used, which serve as an...

Semantic interpretation of the 3D scene is one of the most challenging problems in point cloud processing, which also deems as an essential task in a wide variety of point cloud applications. The core task of semantic interpretation is semantic labeling, namely, obtaining a unique semantic label for each point in the point cloud. Despite several re...

This study had two main aims: (1) to provide a comprehensive review of terrestrial laser scanner (TLS) point cloud registration methods and a better understanding of their strengths and weaknesses; and (2) to provide a large-scale benchmark data set (Wuhan University TLS: Whu-TLS) to support the development of cutting-edge TLS point cloud registrat...

Attitude jitter is a crucial factor that limits the imaging quality and geo-positioning accuracy of high-resolution optical satellites, which has attracted significant research interests in recent years. However, few researchers have attempted to retrieve the dynamic characteristics and time-varying trends of a satellite attitude jitter. This paper...

Dense image matching is a crucial step in many image processing tasks. Subpixel accuracy and fractional measurement are commonly pursued, considering the image resolution and application requirement, especially in the field of remote sensing. In this study, we conducted a practical analysis and comparative study on area-based dense image matching w...

The papers in this special section covers various topics in pattern recognition that deploy remote sensing applications.

3D point cloud generated from a single image has attracted full attention from researchers in the field of multimedia, remote sensing and computer vision. With the recent proliferation of deep learning, various deep models have been proposed for the 3D point cloud generation. However, they require objects to be captured with absolutely clean backgr...