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Ress","J. Meyer","W. Zhang","D. Skuddis","U. Soergel","N. Haala" ], "authors": [ {"first" : "V.", "last" : "Ress"}, {"first" : "J.", "last" : "Meyer"}, {"first" : "W.", "last" : "Zhang"}, {"first" : "D.", "last" : "Skuddis"}, {"first" : "U.", "last" : "Soergel"}, {"first" : "N.", "last" : "Haala"} ], "volume": "XLVIII-G-2025","pages": "1283--1290", "doi" : "10.5194/isprs-archives-XLVIII-G-2025-1283-2025", "bibtexKey": "isprs-archives-XLVIII-G-2025-1283-2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2e6b0ac1350a48d29c6b1f1cdf6b5c7bc/markusenglich", "tags" : [ "conference","review" ], "intraHash" : "e6b0ac1350a48d29c6b1f1cdf6b5c7bc", "interHash" : "1933efcc9c1e3873d3acd3574f53ac96", "label" : "Enhancing Data Quality in Crowdsourcing for Tree Outline Acquisition in Aerial Imagery via CNN-Based Real-Time Feedback", "user" : "markusenglich", "description" : "", "date" : "2025-11-27 07:20:22", "changeDate" : "2025-11-28 08:32:35", "count" : 2, "pub-type": "article", "journal": "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "year": "2025", "url": "https://isprs-annals.copernicus.org/articles/X-G-2025/197/2025/", "author": [ "D. Collmar","V. Walter","U. Sörgel" ], "authors": [ {"first" : "D.", "last" : "Collmar"}, {"first" : "V.", "last" : "Walter"}, {"first" : "U.", "last" : "Sörgel"} ], "volume": "X-G-2025","pages": "197--204","abstract": "We propose a method to improve data quality in paid crowdsourcing by leveraging CNN-based real-time feedback. Data acquired through paid crowdsourcing often suffers from inconsistencies or inaccuracies as workers prioritize task completion speed over precision to maximize earnings. To address this issue, we developed a lightweight, two-branch CNN that evaluates and provides quality feedback on polygon acquisitions of tree outlines in aerial imagery. As workers modify their polygons, the CNN predicts a quality score, displayed as a traffic light signal (red, yellow, green), indicating whether adjustments are needed. Our study compares a test group receiving this feedback with a control group without feedback. Results show that the test group achieves a notably higher average Intersection over Union (IoU) score as well as a lower standard deviation, indicating improved quality and consistency. By integrating the results of multiple workers, the test group achieves even better data quality with fewer samples than the control group. This approach reduces the need for redundant data acquisition, demonstrating its potential for time and cost savings in large-scale data collection campaigns.", "doi" : "10.5194/isprs-annals-X-G-2025-197-2025", "bibtexKey": "isprs-annals-X-G-2025-197-2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/295ea95c2d0f0ca4fc169f9d05c8d6c9a/markusenglich", "tags" : [ "journal","peer","review" ], "intraHash" : "95ea95c2d0f0ca4fc169f9d05c8d6c9a", "interHash" : "126a2b7a692a73930bbe31da24e1733a", "label" : "HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction", "user" : "markusenglich", "description" : "", "date" : "2025-11-24 12:36:05", "changeDate" : "2025-12-09 09:36:04", "count" : 4, "pub-type": "article", "journal": "IEEE Transactions on Robotics", "year": "2025", "url": "", "author": [ "Wei Zhang","Qing Cheng","David Skuddis","Niclas Zeller","Daniel Cremers","Norbert Haala" ], "authors": [ {"first" : "Wei", "last" : "Zhang"}, {"first" : "Qing", "last" : "Cheng"}, {"first" : "David", "last" : "Skuddis"}, {"first" : "Niclas", "last" : "Zeller"}, {"first" : "Daniel", "last" : "Cremers"}, {"first" : "Norbert", "last" : "Haala"} ], "volume": "41","pages": "6478-6493","abstract": "We present HI-SLAM2, a geometry-aware Gaussian SLAM system that achieves fast and accurate monocular scene reconstruction using only RGB input. Existing neural SLAM or 3DGS-based SLAM methods often tradeoff between rendering quality and geometry accuracy, our research demonstrates that both can be achieved simultaneously with RGB input alone. The key idea of our approach is to enhance the ability for geometry estimation by combining easy-to-obtain monocular priors with learning-based dense SLAM, and then using 3-D Gaussian splatting as our core map representation to efficiently model the scene. Upon loop closure, our method ensures on-the-fly global consistency through efficient pose graph bundle adjustment and instant map updates by explicitly deforming the 3-D Gaussian units based on anchored keyframe updates. Furthermore, we introduce a grid-based scale alignment strategy to maintain improved scale consistency in prior depths for finer depth details. Through extensive experiments on Replica, ScanNet, Waymo Open, ETH3D SLAM and ScanNet++ datasets, we demonstrate significant improvements over existing neural SLAM methods and even surpass RGB-D-based methods in both reconstruction and rendering quality.", "doi" : "10.1109/TRO.2025.3626627", "bibtexKey": "11219329" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/23025afee505f1da8d0471e0f9470c3fe/markusenglich", "tags" : [ "conference" ], "intraHash" : "3025afee505f1da8d0471e0f9470c3fe", "interHash" : "27f7f7097d17eff6ddb64d4b50fd1671", "label" : "Dense collaborative mapping with deep visual SLAM method", "user" : "markusenglich", "description" : "", "date" : "2025-07-24 10:01:15", "changeDate" : "2025-07-24 10:01:15", "count" : 3, "pub-type": "article", "journal": "The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "year": "2024", "url": "https://isprs-archives.copernicus.org/articles/XLVIII-1-2024/547/2024/", "author": [ "P. Pan","W. Zhang","N. Haala" ], "authors": [ {"first" : "P.", "last" : "Pan"}, {"first" : "W.", "last" : "Zhang"}, {"first" : "N.", "last" : "Haala"} ], "volume": "XLVIII-1-2024","pages": "547--554", "doi" : "10.5194/isprs-archives-XLVIII-1-2024-547-2024", "bibtexKey": "isprs-archives-XLVIII-1-2024-547-2024" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/254953a926edaeed4cf4fa9c089109ed7/markusenglich", "tags" : [ "conference" ], "intraHash" : "54953a926edaeed4cf4fa9c089109ed7", "interHash" : "b931839621990ceeac3029bb997b763c", "label" : "Multi-scale Transformer-based classification of bathymetric LiDAR data in shallow water environments", "user" : "markusenglich", "description" : "", "date" : "2025-07-08 06:44:06", "changeDate" : "2025-07-08 06:44:06", "count" : 2, "pub-type": "article", "journal": "The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "year": "2025", "url": "https://isprs-archives.copernicus.org/articles/XLVIII-2-W10-2025/1/2025/", "author": [ "L. Asgharian Pournodrati","M. M. Baba","J. Gangelhoff","U. Sörgel" ], "authors": [ {"first" : "L.", "last" : "Asgharian Pournodrati"}, {"first" : "M. M.", "last" : "Baba"}, {"first" : "J.", "last" : "Gangelhoff"}, {"first" : "U.", "last" : "Sörgel"} ], "volume": "XLVIII-2/W10-2025","pages": "1--6", "doi" : "10.5194/isprs-archives-XLVIII-2-W10-2025-1-2025", "bibtexKey": "isprs-archives-XLVIII-2-W10-2025-1-2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/290093ac006a71fe30f08d68ecdb8ff98/markusenglich", "tags" : [ "conference","peer","review" ], "intraHash" : "90093ac006a71fe30f08d68ecdb8ff98", "interHash" : "d3d34c0c6a20303b8361ceec99857412", "label" : "Adaptive Sphere-Based Feature Extraction for Efficient Classification of Bathymetric LiDAR Data", "user" : "markusenglich", "description" : "", "date" : "2025-06-26 07:01:28", "changeDate" : "2025-07-08 06:42:30", "count" : 2, "pub-type": "inproceedings", "booktitle": "Dreiländertagung der DGPF, der OVG und der SGPF in Muttenz, Schweiz","publisher":"Geschäftsstelle der DGPF", "year": "2025", "url": "https://dgpf.de/src/tagung/jt2025/start.html", "author": [ "L. Asgharian Pournodrati","A. Nouri Esfahani","J. Gangelhoff","M. Cramer","U. Soergel" ], "authors": [ {"first" : "L.", "last" : "Asgharian Pournodrati"}, {"first" : "A. Nouri", "last" : "Esfahani"}, {"first" : "J.", "last" : "Gangelhoff"}, {"first" : "M.", "last" : "Cramer"}, {"first" : "U.", "last" : "Soergel"} ], "editor": [ "Thomas P. Kersten & Nora Tilly" ], "editors": [ {"first" : "Thomas P. Kersten & Nora", "last" : "Tilly"} ], "volume": "Publikationen der DGPF","number": "33","pages": "1-9", "issn" : "0942-2870", "bibtexKey": "pournodrati2025adaptive" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/228a769e5119a1b5f5662291b527033e0/markusenglich", "tags" : [ "conference","review" ], "intraHash" : "28a769e5119a1b5f5662291b527033e0", "interHash" : "5c96d28b20dbfbdf6757a9b51caf06ce", "label" : "Error Covariance Analyses for Celestial Triangulation and Its Optimality: Improved Linear Optimal Sine Triangulation", "user" : "markusenglich", "description" : "", "date" : "2025-05-06 11:25:39", "changeDate" : "2025-05-06 11:25:39", "count" : 2, "pub-type": "article", "journal": "Aerospace", "year": "2025", "url": "https://www.mdpi.com/2226-4310/12/5/385", "author": [ "Abdurrahim Muratoglu","Halil Ersin Söken","Uwe Soergel" ], "authors": [ {"first" : "Abdurrahim", "last" : "Muratoglu"}, {"first" : "Halil Ersin", "last" : "Söken"}, {"first" : "Uwe", "last" : "Soergel"} ], "volume": "12","number": "5","abstract": "This study presents an improved methodology for celestial triangulation optimization in spacecraft navigation, addressing limitations in existing approaches. While current methods like Linear Optimal Sine Triangulation (LOST) provide statistically optimal solutions for position estimation using multiple celestial body observations, their performance can be compromised by suboptimal measurement pair selection. The proposed approach, called the Improved-LOST algorithm, introduces a systematic method for evaluating and selecting optimal measurement pairs based on a Cramér\u2013Rao Lower-Bound (CRLB) analysis. Through theoretical analysis and numerical simulations on translunar trajectories, this study demonstrates that geometric configuration significantly influences position estimation accuracy, with error variances varying by orders of magnitude depending on observation geometry. The improved algorithm outperforms conventional implementations, particularly in scenarios with challenging geometric configurations. Simulation results along a translunar trajectory using various celestial body combinations show that the systematic selection of measurement pairs based on CRLB minimization leads to enhanced estimation accuracy compared to arbitrary pair selection. The findings provide valuable insights for autonomous navigation system design and mission planning, offering a quantitative framework for assessing and optimizing celestial triangulation performance in deep space missions.", "issn" : "2226-4310", "article-number" : "385", "doi" : "10.3390/aerospace12050385", "bibtexKey": "aerospace12050385" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2df9d947f8e84e47d699e07c5839a3e72/markusenglich", "tags" : [ "journal","peer","review" ], "intraHash" : "df9d947f8e84e47d699e07c5839a3e72", "interHash" : "5489d3cc628ab465c67706c2c8a0f918", "label" : "HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction", "user" : "markusenglich", "description" : "Under review for IEEE Transactions on Robotics.", "date" : "2025-04-24 07:58:02", "changeDate" : "2025-04-24 07:58:02", "count" : 1, "pub-type": "misc", "year": "2024", "url": "https://arxiv.org/abs/2411.17982", "author": [ "Wei Zhang","Qing Cheng","David Skuddis","Niclas Zeller","Daniel Cremers","Norbert Haala" ], "authors": [ {"first" : "Wei", "last" : "Zhang"}, {"first" : "Qing", "last" : "Cheng"}, {"first" : "David", "last" : "Skuddis"}, {"first" : "Niclas", "last" : "Zeller"}, {"first" : "Daniel", "last" : "Cremers"}, {"first" : "Norbert", "last" : "Haala"} ], "eprint" : "2411.17982", "archiveprefix" : "arXiv", "primaryclass" : "cs.RO", "bibtexKey": "zhang2024hislam2geometryawaregaussianslam" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2dd23ab48b92081dfea1e4ebcd1e40a91/markusenglich", "tags" : [ "conference","peer","review" ], "intraHash" : "dd23ab48b92081dfea1e4ebcd1e40a91", "interHash" : "47fdaf741b0c3140178230cabb55e6c4", "label" : "3D Gaussian Splatting aided Localization for Large and Complex Indoor-Environments", "user" : "markusenglich", "description" : "Accepted for ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences.", "date" : "2025-04-24 07:54:38", "changeDate" : "2025-04-24 07:54:38", "count" : 5, "pub-type": "misc", "year": "2025", "url": "https://arxiv.org/abs/2502.13803", "author": [ "Vincent Ress","Jonas Meyer","Wei Zhang","David Skuddis","Uwe Soergel","Norbert Haala" ], "authors": [ {"first" : "Vincent", "last" : "Ress"}, {"first" : "Jonas", "last" : "Meyer"}, {"first" : "Wei", "last" : "Zhang"}, {"first" : "David", "last" : "Skuddis"}, {"first" : "Uwe", "last" : "Soergel"}, {"first" : "Norbert", "last" : "Haala"} ], "eprint" : "2502.13803", "archiveprefix" : "arXiv", "primaryclass" : "cs.CV", "bibtexKey": "ress20253dgaussiansplattingaided" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2c786c6765e1795b19e5f0da8c80e6892/markusenglich", "tags" : [ "conference","review" ], "intraHash" : "c786c6765e1795b19e5f0da8c80e6892", "interHash" : "e37c92644c6bd351252d20dffca7cbd1", "label" : "Point surfel transformer network for semantic segmentation of large-scale ALS point clouds", "user" : "markusenglich", "description" : "", "date" : "2025-03-14 10:39:15", "changeDate" : "2025-04-17 12:10:55", "count" : 3, "pub-type": "inproceedings", "booktitle": "42. Wissenschaftlich-Technische Jahrestagung der DGPF, 5.-6. Oktober 2022 in Dresden","publisher":"Geschäftsstelle der DGPF;","address":"Stuttgart", "year": "2022", "url": "https://www.tib.eu/de/suchen/id/TIBKAT%3A1796046701", "author": [ "Xinlong Zhang","Ruihang Xue","Michael Kölle","Uwe Sörgel" ], "authors": [ {"first" : "Xinlong", "last" : "Zhang"}, {"first" : "Ruihang", "last" : "Xue"}, {"first" : "Michael", "last" : "Kölle"}, {"first" : "Uwe", "last" : "Sörgel"} ], "pages": "290-296", "doi" : "10.24407/KXP:1796046701", "bibtexKey": "TIBKAT:1796046701" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/281dcdc2a16192c0689afd0d94cbbff6a/markusenglich", "tags" : [ "journal","peer","review","rp26" ], "intraHash" : "81dcdc2a16192c0689afd0d94cbbff6a", "interHash" : "ad44c48c3cae924b82b9e85476fa4b92", "label" : "Target-aware attentional network for rare class segmentation in large-scale LiDAR point clouds", "user" : "markusenglich", "description" : "", "date" : "2025-03-14 10:32:00", "changeDate" : "2025-04-17 12:14:19", "count" : 3, "pub-type": "article", "journal": "ISPRS Journal of Photogrammetry and Remote Sensing", "year": "2025", "url": "https://www.sciencedirect.com/science/article/pii/S0924271624004222", "author": [ "Xinlong Zhang","Dong Lin","Uwe Soergel" ], "authors": [ {"first" : "Xinlong", "last" : "Zhang"}, {"first" : "Dong", "last" : "Lin"}, {"first" : "Uwe", "last" : "Soergel"} ], "volume": "220","pages": "32-50","abstract": "Semantic interpretation of 3D scenes poses a formidable challenge in point cloud processing, which also stands as a requisite undertaking across various fields of application involving point clouds. Although a number of point cloud segmentation methods have achieved leading performance, 3D rare class segmentation continues to be a challenge owing to the imbalanced distribution of fine-grained classes and the complexity of large scenes. In this paper, we present target-aware attentional network (TaaNet), a novel mask-constrained attention framework to address 3D semantic segmentation of imbalanced classes in large-scale point clouds. Adapting the self-attention mechanism, a hierarchical aggregation strategy is first applied to enhance the learning of point-wise features across various scales, which leverages both global and local perspectives to guarantee presence of fine-grained patterns in the case of scenes with high complexity. Subsequently, rare target masks are imposed by a contextual module on the hierarchical features. Specifically, a target-aware aggregator is proposed to boost discriminative features of rare classes, which constrains hierarchical features with learnable adaptive weights and simultaneously embeds confidence constraints of rare classes. Furthermore, a target pseudo-labeling strategy based on strong contour cues of rare classes is designed, which effectively delivers instance-level supervisory signals restricted to rare targets only. We conducted thorough experiments on four multi-platform LiDAR benchmarks, i.e., airborne, mobile and terrestrial platforms, to assess the performance of our framework. Results demonstrate that compared to other commonly used advanced segmentation methods, our method can obtain not only high segmentation accuracy but also remarkable F1-scores in rare classes. In a submission to the official ranking page of Hessigheim 3D benchmark, our approach achieves a state-of-the-art mean F1-score of 83.84% and an outstanding overall accuracy (OA) of 90.45%. In particular, the F1-scores of rare classes namely vehicles and chimneys notably exceed the average of other published methods by a wide margin, boosting by 32.00% and 32.46%, respectively. Additionally, extensive experimental analysis on benchmarks collected from multiple platforms, Paris-Lille-3D, Semantic3D and WHU-Urban3D, validates the robustness and effectiveness of the proposed method.", "issn" : "0924-2716", "doi" : "https://doi.org/10.1016/j.isprsjprs.2024.11.012", "bibtexKey": "ZHANG202532" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2cf755228b87525f556965634835c800c/markusenglich", "tags" : [ "conference","peer","review","rp26" ], "intraHash" : "cf755228b87525f556965634835c800c", "interHash" : "cf3e6eb263b0fe1b939c4e0f4787d6ef", "label" : "Crowd Controls Crowd: Quality Improvement of Polygon Integration in Paid Crowdsourcing", "user" : "markusenglich", "description" : "", "date" : "2024-10-22 07:38:29", "changeDate" : "2024-11-05 06:37:53", "count" : 3, "pub-type": "article", "journal": "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "year": "2024", "url": "https://isprs-annals.copernicus.org/articles/X-4-2024/83/2024/", "author": [ "D. Collmar","V. Walter","U. Soergel" ], "authors": [ {"first" : "D.", "last" : "Collmar"}, {"first" : "V.", "last" : "Walter"}, {"first" : "U.", "last" : "Soergel"} ], "volume": "X-4-2024","pages": "83--90", "doi" : "10.5194/isprs-annals-X-4-2024-83-2024", "bibtexKey": "isprs-annals-X-4-2024-83-2024" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2ff509bc1e4f12fd7cf36d003a12ba669/markusenglich", "tags" : [ "conference","review" ], "intraHash" : "ff509bc1e4f12fd7cf36d003a12ba669", "interHash" : "f54de9e7d819e211abaf3abf14c3ca06", "label" : "DMSA - Dense Multi Scan Adjustment for LiDAR Inertial Odometry and Global Optimization", "user" : "markusenglich", "description" : "", "date" : "2024-09-18 09:20:36", "changeDate" : "2024-09-18 09:20:36", "count" : 3, "pub-type": "inproceedings", "booktitle": "2024 IEEE International Conference on Robotics and Automation (ICRA)", "year": "2024", "url": "", "author": [ "David Skuddis","Norbert Haala" ], "authors": [ {"first" : "David", "last" : "Skuddis"}, {"first" : "Norbert", "last" : "Haala"} ], "pages": "12027-12033", "doi" : "10.1109/ICRA57147.2024.10610818", "bibtexKey": "10610818" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2f634ef749b1250f232d0b9d562a1ee33/markusenglich", "tags" : [ "conference","review" ], "intraHash" : "f634ef749b1250f232d0b9d562a1ee33", "interHash" : "c7178c297a2caad0221ab8f5569d39f9", "label" : "Neural Surface Reconstruction: A Game Changer for 3D Data Collection from Airborne Imagery?", "user" : "markusenglich", "description" : "", "date" : "2024-09-11 11:26:11", "changeDate" : "2024-09-12 10:55:19", "count" : 3, "pub-type": "inproceedings", "booktitle": "IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium", "year": "2024", "url": "", "author": [ "V. Hackstein","M. Rothermel","N. Haala" ], "authors": [ {"first" : "V.", "last" : "Hackstein"}, {"first" : "M.", "last" : "Rothermel"}, {"first" : "N.", "last" : "Haala"} ], "pages": "2619--2624", "issn" : "21537003", "doi" : "10.1109/IGARSS53475.2024.10640720", "bibtexKey": "hackstein712neural" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/220305c11e08658f21480a5c063c88692/markusenglich", "tags" : [ "conference","peer","review" ], "intraHash" : "20305c11e08658f21480a5c063c88692", "interHash" : "5b04c4c79d6a6c8e05b899fba74129d0", "label" : "Narrowing the Synthetic-to-Real Gap for Thermal Infrared Semantic Image Segmentation Using Diffusion-based Conditional Image Synthesis", "user" : "markusenglich", "description" : "", "date" : "2024-07-05 08:56:04", "changeDate" : "2024-07-05 09:13:22", "count" : 2, "pub-type": "inproceedings", "booktitle": "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops", "year": "2024", "url": "https://openaccess.thecvf.com/content/CVPR2024W/PBVS/html/Mayr_Narrowing_the_Synthetic-to-Real_Gap_for_Thermal_Infrared_Semantic_Image_Segmentation_CVPRW_2024_paper.html", "author": [ "Christian Mayr","Christian Kubler","Norbert Haala","Michael Teutsch" ], "authors": [ {"first" : "Christian", "last" : "Mayr"}, {"first" : "Christian", "last" : "Kubler"}, {"first" : "Norbert", "last" : "Haala"}, {"first" : "Michael", "last" : "Teutsch"} ], "pages": "3131-3141", "bibtexKey": "Mayr_2024_CVPR" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/220a96bae8fcf7224ff64228dfbdfcefe/markusenglich", "tags" : [ "conference","review" ], "intraHash" : "20a96bae8fcf7224ff64228dfbdfcefe", "interHash" : "4e1397064c9bb67e43c6442885dc2dbc", "label" : "Depth Supervised Neural Surface Reconstruction from Airborne Imagery", "user" : "markusenglich", "description" : "", "date" : "2024-06-13 11:42:23", "changeDate" : "2024-06-13 11:42:23", "count" : 3, "pub-type": "article", "journal": "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "year": "2024", "url": "https://isprs-annals.copernicus.org/articles/X-2-2024/89/2024/", "author": [ "V. Hackstein","P. Fauth-Mayer","M. Rothermel","N. Haala" ], "authors": [ {"first" : "V.", "last" : "Hackstein"}, {"first" : "P.", "last" : "Fauth-Mayer"}, {"first" : "M.", "last" : "Rothermel"}, {"first" : "N.", "last" : "Haala"} ], "volume": "X-2-2024","pages": "89--96","abstract": "While originally developed for novel view synthesis, Neural Radiance Fields (NeRFs) have recently emerged as an alternative to multi-view stereo (MVS). Triggered by a manifold of research activities, promising results have been gained especially for textureless, transparent, and reflecting surfaces, while such scenarios remain challenging for traditional MVS-based approaches. However, most of these investigations focus on close-range scenarios, with studies for airborne scenarios still missing. For this task, NeRFs face potential difficulties at areas of low image redundancy and weak data evidence, as often found in street canyons, facades or building shadows. Furthermore, training such networks is computationally expensive. Thus, the aim of our work is twofold: First, we investigate the applicability of NeRFs for aerial image blocks representing different characteristics like nadir-only, oblique and high-resolution imagery. Second, during these investigations we demonstrate the benefit of integrating depth priors from tie-point measures, which are provided during presupposed Bundle Block Adjustment. Our work is based on the state-of-the-art framework VolSDF, which models 3D scenes by signed distance functions (SDFs), since this is more applicable for surface reconstruction compared to the standard volumetric representation in vanilla NeRFs. For evaluation, the NeRF-based reconstructions are compared to results of a publicly available benchmark dataset for airborne images.", "doi" : "10.5194/isprs-annals-X-2-2024-89-2024", "bibtexKey": "isprs-annals-X-2-2024-89-2024" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2514f594c38477d1e1e28dc891b45b6f2/markusenglich", "tags" : [ "conference","peer","review","rp26" ], "intraHash" : "514f594c38477d1e1e28dc891b45b6f2", "interHash" : "592528bd27433f0eb8b4a3b5393f4c24", "label" : "SLAM for Indoor Mapping of Wide Area Construction Environments", "user" : "markusenglich", "description" : "", "date" : "2024-06-13 11:40:39", "changeDate" : "2024-06-13 11:40:39", "count" : 3, "pub-type": "article", "journal": "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences", "year": "2024", "url": "https://isprs-annals.copernicus.org/articles/X-2-2024/209/2024/", "author": [ "V. Ress","W. Zhang","D. Skuddis","N. Haala","U. Soergel" ], "authors": [ {"first" : "V.", "last" : "Ress"}, {"first" : "W.", "last" : "Zhang"}, {"first" : "D.", "last" : "Skuddis"}, {"first" : "N.", "last" : "Haala"}, {"first" : "U.", "last" : "Soergel"} ], "volume": "X-2-2024","pages": "209--216","abstract": "Simultaneous localization and mapping (SLAM), i.e., the reconstruction of the environment represented by a (3D) map and the concurrent pose estimation, has made astonishing progress. Meanwhile, large scale applications aiming at the data collection in complex environments like factory halls or construction sites are becoming feasible. However, in contrast to small scale scenarios with building interiors separated to single rooms, shop floors or construction areas require measures at larger distances in potentially texture less areas under difficult illumination. Pose estimation is further aggravated since no GNSS measures are available as it is usual for such indoor applications. In our work, we realize data collection in a large factory hall by a robot system equipped with four stereo cameras as well as a 3D laser scanner. We apply our state-of-the-art LiDAR and visual SLAM approaches and discuss the respective pros and cons of the different sensor types for trajectory estimation and dense map generation in such an environment. Additionally, dense and accurate depth maps are generated by 3D Gaussian splatting, which we plan to use in the context of our project aiming on the automatic construction and site monitoring.", "doi" : "10.5194/isprs-annals-X-2-2024-209-2024", "bibtexKey": "isprs-annals-X-2-2024-209-2024" } ] }