Generalization Evaluation of Deep Stereo Matching Methods for UAV-Based Forestry Applications
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；Victoria University of Wellington（惠灵顿维多利亚大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；University of Canterbury（坎特伯雷大学）
Yida Lin, Bing Xue, Mengjie Zhang, Sam Schofield, Richard Green
Autonomous UAV forestry operations require robust depth estimation methods with strong cross-domain generalization. However, existing evaluations focus on urban and indoor scenarios, leaving a critical gap for specialized vegetation-dense environments. We present the first systematic zero-shot evaluation of eight state-of-the-art stereo methods--RAFT-Stereo, IGEV, IGEV++, BridgeDepth, StereoAnywhere, DEFOM (plus baseline methods ACVNet, PSMNet, TCstereo)--spanning iterative refinement, foundation model, and zero-shot adaptation paradigms. All methods are trained exclusively on Scene Flow and evaluated without fine-tuning on four standard benchmarks (ETH3D, KITTI 2012/2015, Middlebury) plus a novel 5,313-pair Canterbury forestry dataset captured with ZED Mini camera (1920x1080). Performance reveals scene-dependent patterns: foundation models excel on structured scenes (BridgeDepth: 0.23 px on ETH3D, 0.83-1.07 px on KITTI; DEFOM: 0.35-4.65 px across benchmarks), while iterative methods maintain cross-domain robustness (IGEV++: 0.36-6.77 px; IGEV: 0.33-21.91 px). Critical finding: RAFT-Stereo exhibits catastrophic ETH3D failure (26.23 px EPE, 98 percent error rate) due to negative disparity predictions, while performing normally on KITTI (0.90-1.11 px). Qualitative evaluation on Canterbury forestry dataset identifies DEFOM as the optimal gold-standard baseline for vegetation depth estimation, exhibiting superior depth smoothness, occlusion handling, and cross-domain consistency compared to IGEV++, despite IGEV++'s finer detail preservation.
Progressive Per-Branch Depth Optimization for DEFOM-Stereo and SAM3 Joint Analysis in UAV Forestry Applications
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；Victoria University of Wellington（威灵顿维多利亚大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；University of Canterbury（坎特伯雷大学）
PLA Strategic Support Force Information Engineering University（中国人民解放军战略支援部队信息工程大学）；Shenzhen University（深圳大学）
Stereo matching is a fundamental task for 3D scene reconstruction. Recently, deep learning based methods have proven effective on some benchmark datasets, such as KITTI and Scene Flow. UAVs (Unmanned Aerial Vehicles) are commonly utilized for surface observation, and their captured images are frequently used for detailed 3D reconstruction due to high resolution and low-altitude acquisition. At present, the mainstream supervised learning network requires a significant amount of training data with ground-truth labels to learn model parameters. However, due to the scarcity of UAV stereo matching …
Towards Gold-Standard Depth Estimation for Tree Branches in UAV Forestry: Benchmarking Deep Stereo Matching Methods
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；Victoria University of Wellington（惠灵顿维多利亚大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；University of Canterbury（坎特伯雷大学）
FaSS-MVS -- Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-borne Monocular Imagery
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB)（弗劳恩霍夫光学技术、系统技术与图像利用研究所）；Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
Institute for Computer Graphics and Vision, Graz University of Technology（格拉茨工业大学计算机图形与视觉研究所）；OMICRON electronics GmbH（奥米克龙电子有限公司）
In this paper we present an autonomous system for acquiring close-range high-resolution images that maximize the quality of a later-on 3D reconstruction with respect to coverage, ground resolution and 3D uncertainty. In contrast to previous work, our system uses the already acquired images to predict the confidence in the output of a dense multi-view stereo approach without executing it. This confidence encodes the likelihood of a successful reconstruction with respect to the observed scene and potential camera constellations. Our prediction module runs in real-time and can be trained without …
Singapore-ETH Center（新加坡-ETH中心）；ETH Zuerich（苏黎世联邦理工学院）；University of Melbourne（墨尔本大学）
Recently developed automatic dense image matching algorithms are now being implemented for DSM/DTM production, with their pixel-level surface generation capability offering the prospect of partially alleviating the need for manual and semi-automatic stereoscopic measurements. In this paper, five commercial/public software packages for 3D surface generation are evaluated, using 5cm GSD imagery recorded from a UAV. Generated surface models are assessed against point clouds generated from mobile LiDAR and manual stereoscopic measurements. The software packages considered are APS, MICMAC, SURE, Pi…
YOLO and SGBM Integration for Autonomous Tree Branch Detection and Depth Estimation in Radiata Pine Pruning Applications
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；Victoria University of Wellington（惠灵顿维多利亚大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；University of Canterbury（坎特伯雷大学）
LeafInst - Unified Instance Segmentation Network for Fine-Grained Forestry Leaf Phenotype Analysis: A New UAV based Benchmark
organization= College of Information Science；Technology \& Artificial Intelligence, Nanjing Forestry University , city= Nanjing , postcode= 210037 , state= Jiangsu , country= China；organization= Department of Geography, College of Natural Resources；organization= Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University , city= Nanjing , postcode= 210037 , state= Jiangsu , country= China；organization= School of Architecture \& Urban Planning, Shenzhen University , city= Shenzhen , postcode= 518060 , state= Guangdong , country= China；organization= School of Software, Hefei University of Technology , city= Hefei , postcode= 230601 , state= Anhui , country= China
Training Deep Stereo Matching Networks on Tree Branch Imagery: A Benchmark Study for Real-Time UAV Forestry Applications
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；Victoria University of Wellington（威灵顿维多利亚大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；University of Canterbury（坎特伯雷大学）
Fraunhofer IOSB（弗劳恩霍夫光电、系统技术及图像处理研究所）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
Real-time 3D reconstruction enables fast dense mapping of the environment which benefits numerous applications, such as navigation or live evaluation of an emergency. In contrast to most real-time capable approaches, our approach does not need an explicit depth sensor. Instead, we only rely on a video stream from a camera and its intrinsic calibration. By exploiting the self-motion of the unmanned aerial vehicle (UAV) flying with oblique view around buildings, we estimate both camera trajectory and depth for selected images with enough novel content. To create a 3D model of the scene, we rely …
University of Applied Science, Gelsenkirchen（盖尔森基兴应用科学大学）；German Rescue Robotic Centre, Dortmund（多特蒙德德国救援机器人中心）
This work proposes a new method for real-time dense 3d reconstruction for common 360° action cams, which can be mounted on small scouting UAVs during USAR missions. The proposed method extends a feature based Visual monocular SLAM (OpenVSLAM, based on the popular ORB-SLAM) for robust long-term localization on equirectangular video input by adding an additional densification thread that computes dense correspondences for any given keyframe with respect to a local keyframe-neighboorhood using a PatchMatch-Stereo-approach. While PatchMatch-Stereo-types of algorithms are considered state of the ar…
National Technical University of Athens（雅典国家技术大学）；Cyprus University of Technology（塞浦路斯理工大学）
The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this pro…
Revisiting Stereo Triangulation in UAV Distance Estimation
Shantou University（汕头大学）；Key Lab of Digital Signal and Image Processing of Guangdong Province（广东省数字信号与图像处理重点实验室）；College of Electronic Engineering, Shantou University（汕头大学电子工程学院）
Performance Evaluation of Deep Learning for Tree Branch Segmentation in Autonomous Forestry Systems
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；Victoria University of Wellington（惠灵顿维多利亚大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；University of Canterbury（坎特伯雷大学）
Uncertainty Quantification Framework for Aerial and UAV Photogrammetry through Error Propagation
Geospatial Data Analytics Laboratory, The Ohio State University（地理空间数据分析实验室，俄亥俄州立大学）；Department of Civil, Environmental and Geodetic Engineering, The Ohio State University（土木、环境与大地测量工程系，俄亥俄州立大学）；Department of Electrical and Computer Engineering, The Ohio State University（电气与计算机工程系，俄亥俄州立大学）；Translational Data Analytics Institute, The Ohio State University（转化数据分析研究院，俄亥俄州立大学）
RASR: Range-Aware Scale Recovery for Metric UAV Navigation
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学电子信息工程学院）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Noah Ark Lab, Huawei（华为诺亚方舟实验室）；College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化工程学院）；College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB)（弗劳恩霍夫光学、系统技术与图像 exploitation 研究所）；Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院摄影测量与遥感研究所）；Vision and Fusion Laboratory, Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院视觉与融合实验室）
With the emergence of low-cost robotic systems, such as unmanned aerial vehicle, the importance of embedded high-performance image processing has increased. For a long time, FPGAs were the only processing hardware that were capable of high-performance computing, while at the same time preserving a low power consumption, essential for embedded systems. However, the recently increasing availability of embedded GPU-based systems, such as the NVIDIA Jetson series, comprised of an ARM CPU and a NVIDIA Tegra GPU, allows for massively parallel embedded computing on graphics hardware. With this in min…
University of Stuttgart（斯图加特大学）；Leibniz University Hannover（汉诺威莱布尼茨大学）；University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Delft University of Technology（代尔夫特理工大学）
Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially in the geospatial domain, such datasets are quite scarce. Within this paper, we aim to alleviate this issue by introducing a new annotated 3D dataset that is unique in three ways: i) The dataset consists of both an Unmanned Aerial Vehicle (UAV) laser scanning point cloud and a 3D textured mesh. ii) The point cloud features a mean point density of a…
The condition assessment of road surfaces is essential to ensure their serviceability while still providing maximum road traffic safety. This paper presents a robust stereo vision system embedded in an unmanned aerial vehicle (UAV). The perspective view of the target image is first transformed into the reference view, and this not only improves the disparity accuracy, but also reduces the algorithm's computational complexity. The cost volumes generated from stereo matching are then filtered using a bilateral filter. The latter has been proved to be a feasible solution for the functional minimi…
University of Oxford（牛津大学）；FiveAI Ltd.（FiveAI有限公司）
Stereo depth estimation is used for many computer vision applications. Though many popular methods strive solely for depth quality, for real-time mobile applications (e.g. prosthetic glasses or micro-UAVs), speed and power efficiency are equally, if not more, important. Many real-world systems rely on Semi-Global Matching (SGM) to achieve a good accuracy vs. speed balance, but power efficiency is hard to achieve with conventional hardware, making the use of embedded devices such as FPGAs attractive for low-power applications. However, the full SGM algorithm is ill-suited to deployment on FPGAs…
We present a novel stereo vision algorithm that is capable of obstacle detection on a mobile-CPU processor at 120 frames per second. Our system performs a subset of standard block-matching stereo processing, searching only for obstacles at a single depth. By using an onboard IMU and state-estimator, we can recover the position of obstacles at all other depths, building and updating a full depth-map at framerate. Here, we describe both the algorithm and our implementation on a high-speed, small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system requires no external sensing or com…
Genetic Algorithms For Parameter Optimization for Disparity Map Generation of Radiata Pine Branch Images
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；Victoria University of Wellington（惠灵顿维多利亚大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；University of Canterbury（坎特伯雷大学）
German Aerospace Center (DLR), Institute of Space Research（德国航空航天中心（DLR）空间研究所）；Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente（代尔夫特理工大学地理信息科学与地球观测学院）
Scale-Aware UAV-to-Satellite Cross-View Geo-Localization: A Semantic Geometric Approach
College of Aerospace Science and Engineering, National University of Defense Technology（航天工程学院，国防科技大学）；Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation（湖南省图像测量与视觉导航重点实验室）；School of Information Science and Engineering, Lanzhou University（信息科学与工程学院，兰州大学）
Unmanned aerial vehicles (UAVs) have been used for many applications in recent years, from urban search and rescue, to agricultural surveying, to autonomous underground mine exploration. However, deploying UAVs in tight, indoor spaces, especially close to humans, remains a challenge. One solution, when limited payload is required, is to use micro-UAVs, which pose less risk to humans and typically cost less to replace after a crash. However, micro-UAVs can only carry a limited sensor suite, e.g. a monocular camera instead of a stereo pair or LiDAR, complicating tasks like dense mapping and mark…
