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Found 35 papers, 6 papers with code
Image Fusion in Remote Sensing: An Overview and Meta Analysis
Image fusion in Remote Sensing (RS) has been a consistent demand due to its ability to turn raw images of different resolutions, sources, and modalities into accurate, complete, and spatio-temporally coherent images.
On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data
Yet, there is no comprehensive analysis of their transferability, i. e., to which extent a model trained on a source domain can be readily applicable to a target domain.
Multi-tiling Neural Radiance Field (NeRF) -- Geometric Assessment on Large-scale Aerial Datasets
Neural Radiance Fields (NeRF) offer the potential to benefit 3D reconstruction tasks, including aerial photogrammetry.
A Critical Analysis of Internal Reliability for Uncertainty Quantification of Dense Image Matching in Multi-view Stereo
Typically, photogrammetric point clouds are assessed through reference data such as LiDAR point clouds.
Mesh Conflation of Oblique Photogrammetric Models using Virtual Cameras and Truncated Signed Distance Field
Conflating/stitching 2. 5D raster digital surface models (DSM) into a large one has been a running practice in geoscience applications, however, conflating full-3D mesh models, such as those from oblique photogrammetry, is extremely challenging.
Select-and-Combine (SAC): A Novel Multi-Stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets
However, the noises and outliers caused by stereo matching and the heterogenous geometric errors of the poses present a challenge for existing fusion algorithms, since they mostly assume Gaussian errors and predict fused results based on data from local spatial neighborhoods, which may inherit uncertainties from multiple depths resulting in lowered accuracy.
Large-scale and Efficient Texture Mapping Algorithm via Loopy Belief Propagation
Texture mapping as a fundamental task in 3D modeling has been well established for well-acquired aerial assets under consistent illumination, yet it remains a challenge when it is scaled to large datasets with images under varying views and illuminations.
Point Cloud Registration for LiDAR and Photogrammetric Data: a Critical Synthesis and Performance Analysis on Classic and Deep Learning Algorithms
In this work, we provide a comprehensive review of the state-of-the-art (SOTA) point cloud registration methods, where we analyze and evaluate these methods using a diverse set of point cloud data from indoor to satellite sources.
A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images
All DL algorithms are robust to geometric configurations of stereo pairs and are less sensitive in comparison to the Census-SGM, while learning-based cost metrics can generalize on satellite images when trained on different datasets (airborne or ground-view).
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems
The increase in the measured performance, however, can have two possible attributions: a better understanding of user preferences, and a more proactive ability to utilize human bounded rationality to seduce user over-consumption.
A Review of Mobile Mapping Systems: From Sensors to Applications
The evolution of mobile mapping systems (MMSs) has gained more attention in the past few decades.
Fine-tuning deep learning models for stereo matching using results from semi-global matching
Knowing that classical stereo matching methods such as Census-based semi-global-matching (SGM) are widely adopted to process different types of stereo data, we therefore, propose a finetuning method that takes advantage of disparity maps derived from SGM on target stereo data.
A Novel Intrinsic Image Decomposition Method to Recover Albedo for Aerial Images in Photogrammetry Processing
Recovering surface albedos from photogrammetric images for realistic rendering and synthetic environments can greatly facilitate its downstream applications in VR/AR/MR and digital twins.
Constrained Bundle Adjustment for Structure From Motion Using Uncalibrated Multi-Camera Systems
We performed our bundle adjustment using the proposed constraint and then produced 3D dense point clouds.
Investigating Spherical Epipolar Rectification for Multi-View Stereo 3D Reconstruction
However, existing approaches face challenges in applying dense matching for images with different viewpoints primarily due to large differences in object scale.
Sat2lod2: A Software For Automated Lod-2 Modeling From Satellite-Derived Orthophoto And Digital Surface Model
We further improve the robustness of the method by 1) intergrading building segmentation based on HRNetV2 into our software; and 2) having implemented a decision strategy to identify complex buildings and directly generate mesh to avoid erroneous LoD2 reconstruction from a system point of view.
Multi-Agent Policy Transfer via Task Relationship Modeling
We demonstrate that the task representation can capture the relationship among tasks, and can generalize to unseen tasks.
A Graph-Matching Approach for Cross-view Registration of Over-view 2 and Street-view based Point Clouds
In this paper, based on the assumption that the object boundaries (e. g., buildings) from the over-view data should coincide with footprints of fa\c{c}ade 3D points generated from street-view photogrammetric images, we aim to address this problem by proposing a fully automated geo-registration method for cross-view data, which utilizes semantically segmented object boundaries as view-invariant features under a global optimization framework through graph-matching: taking the over-view point clouds generated from stereo/multi-stereo satellite images and the street-view point clouds generated from monocular video images as the inputs, the proposed method models segments of buildings as nodes of graphs, both detected from the satellite-based and street-view based point clouds, thus to form the registration as a graph-matching problem to allow non-rigid matches; to enable a robust solution and fully utilize the topological relations between these segments, we propose to address the graph-matching problem on its conjugate graph solved through a belief-propagation algorithm.
A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit,Model Scalability and Transferability
As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis.
Automated LoD-2 Model Reconstruction from Very-HighResolution Satellite-derived Digital Surface Model and Orthophoto
In this paper, we propose a model-driven method that reconstructs LoD-2 building models following a "decomposition-optimization-fitting" paradigm.
Quality assessment of image matchers for DSM generation -- a comparative study based on UAV images
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.
Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility
Then the visibility information of multiple views is aggregated to generate a 3D mesh model by solving an optimization problem considering visibility in which a novel adaptive visibility weighting in surface determination is also introduced to suppress line of sight with a large incident angle.
A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse
In this paper, we present a case study that performs an unmanned aerial vehicle (UAV) based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event.
Spatiotemporal Fusion in Remote Sensing
Remote sensing images and techniques are powerful tools to investigate earth surface.
3D Iterative Spatiotemporal Filtering for Classification of Multitemporal Satellite Data Sets
The current practice in land cover/land use change analysis relies heavily on the individually classified maps of the multitemporal data set.
A Unified Framework of Bundle Adjustment and Feature Matching for High-Resolution Satellite Images
Bundle adjustment (BA) is a technique for refining sensor orientations of satellite images, while adjustment accuracy is correlated with feature matching results.
Individual Tree Detection and Crown Delineation with 3D Information from Multi-view Satellite Images
Individual tree detection and crown delineation (ITDD) are critical in forest inventory management and remote sensing based forest surveys are largely carried out through satellite images.
Change Detection for Geodatabase Updating
This article aims to provide an overview the state-of-the-art change detection methods in the field of Remote Sensing and Geomatics to support the task of updating geodatabases.
3D Reconstruction through Fusion of Cross-View Images
3D recovery from multi-stereo and stereo images, as an important application of the image-based perspective geometry, serves many applications in computer vision, remote sensing and Geomatics.
Geometric Processing for Image-based 3D Object Modeling
Nowadays the image-based methods backboned by the recently developed advanced dense image matching algorithms and geo-referencing paradigms, are becoming the dominant approaches, due to its high flexibility, availability and low cost.
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
We evaluate existing offline RL algorithms on NeoRL and argue that the performance of a policy should also be compared with the deterministic version of the behavior policy, instead of the dataset reward.
Sat2Vid: Street-view Panoramic Video Synthesis from a Single Satellite Image
For geometrical and temporal consistency, our approach explicitly creates a 3D point cloud representation of the scene and maintains dense 3D-2D correspondences across frames that reflect the geometric scene configuration inferred from the satellite view.
A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images
Stereo dense image matching can be categorized to low-level feature based matching and deep feature based matching according to their matching cost metrics.
Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping
The intersection angle between two images are normally seen as the most important one in stereo data acquisition, as the state-of-the-art DIM algorithms work best on narrow baseline (smaller intersection angle) stereos (E. g. Semi-Global Matching regards 15-25 degrees as good intersection angle).
Automated 3D recovery from very high resolution multi-view satellite images
Multiple depth maps derived from individual image pairs are fused with an adaptive 3D median filter that considers the image spectral similarities.
