57 citations · 138 across the 50 of their papers we have counts for
55 papers
SkyAnchor: Updating Metric-scale Aerial 3D Gaussian Scenes from Unposed Ground-View Sequences
Zhuoxiao Li, Xinyi Liu, Taoyu Wu +8
We study how to update a pre-built aerial scene with a newly captured, unposed ground-view sequence. The aerial scene already contains a reliable metric Structure-from-Motion (SfM)…
SatSplat: Geometrically-Accurate Gaussian Splatting for Satellite Imagery
Shuang Song, Jiyong Kim, Rongjun Qin
High-resolution satellite imagery demands 3D reconstruction methods that deliver both speed and geometric accuracy. Recent adaptations of 3D Gaussian Splatting (3DGS) to satellite…
Feed-Forward Gaussian Splatting from Sparse Aerial Views
Dongli Wu, Zhuoxiao Li, Tongyan Hua +4
Reconstructing large-scale urban scenes from sparse aerial views is a crucial yet challenging task. Due to biased top-down and shallow-oblique camera poses, sparse aerial captures…
Olbedo: An Albedo and Shading Aerial Dataset for Large-Scale Outdoor Environments
Shuang Song, Debao Huang, Deyan Deng +4
Intrinsic image decomposition (IID) of outdoor scenes is crucial for relighting, editing, and understanding large-scale environments, but progress has been limited by the lack of r…
Accurate Point Measurement in 3DGS -- A New Alternative to Traditional Stereoscopic-View Based Measurements
Deyan Deng, Rongjun Qin
3D Gaussian Splatting (3DGS) has revolutionized real-time rendering with its state-of-the-art novel view synthesis, but its utility for accurate geometric measurement remains under…
AeroDGS: Physically Consistent Dynamic Gaussian Splatting for Single-Sequence Aerial 4D Reconstruction
Hanyang Liu, Rongjun Qin
Recent advances in 4D scene reconstruction have significantly improved dynamic modeling across various domains. However, existing approaches remain limited under aerial conditions…
Beyond a Single Light: A Large-Scale Aerial Dataset for Urban Scene Reconstruction Under Varying Illumination
Zhuoxiao Li, Wenzong Ma, Taoyu Wu +8
Recent advances in Neural Radiance Fields and 3D Gaussian Splatting have demonstrated strong potential for large-scale UAV-based 3D reconstruction tasks by fitting the appearance o…
Cross-view Localization and Synthesis -- Datasets, Challenges and Opportunities
Ningli Xu, Rongjun Qin
Cross-view localization and synthesis are two fundamental tasks in cross-view visual understanding, which deals with cross-view datasets: overhead (satellite or aerial) and ground-…
Deep learning for 3D point cloud processing -- from approaches, tasks to its implications on urban and environmental applications
Zhenxin Zhang, Zhihua Xu, Yuwei Cao +5
Point cloud processing as a fundamental task in the field of geomatics and computer vision, has been supporting tasks and applications at different scales from air to ground, inclu…
Synthetic Data Matters: Re-training with Geo-typical Synthetic Labels for Building Detection
Shuang Song, Yang Tang, Rongjun Qin
Deep learning has significantly advanced building segmentation in remote sensing, yet models struggle to generalize on data of diverse geographic regions due to variations in city…
An Evaluation of DUSt3R/MASt3R/VGGT 3D Reconstruction on Photogrammetric Aerial Blocks
Xinyi Wu, Steven Landgraf, Markus Ulrich +1
State-of-the-art 3D computer vision algorithms continue to advance in handling sparse, unordered image sets. Recently developed foundational models for 3D reconstruction, such as D…
Uncertainty Quantification Framework for Aerial and UAV Photogrammetry through Error Propagation
Debao Huang, Rongjun Qin
Uncertainty quantification of the photogrammetry process is essential for providing per-point accuracy credentials of the point clouds. Unlike airborne LiDAR, whose accuracy genera…
Satellite to GroundScape -- Large-scale Consistent Ground View Generation from Satellite Views
Ningli Xu, Rongjun Qin
Generating consistent ground-view images from satellite imagery is challenging, primarily due to the large discrepancies in viewing angles and resolution between satellite and grou…
A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation
Steven Landgraf, Rongjun Qin, Markus Ulrich
While recent foundation models have enabled significant breakthroughs in monocular depth estimation, a clear path towards safe and reliable deployment in the real-world remains elu…
Skyeyes: Ground Roaming using Aerial View Images
Zhiyuan Gao, Wenbin Teng, Gonglin Chen +5
Integrating aerial imagery-based scene generation into applications like autonomous driving and gaming enhances realism in 3D environments, but challenges remain in creating detail…
A General Albedo Recovery Approach for Aerial Photogrammetric Images through Inverse Rendering
Shuang Song, Rongjun Qin
Modeling outdoor scenes for the synthetic 3D environment requires the recovery of reflectance/albedo information from raw images, which is an ill-posed problem due to the complicat…
Deep Learning Meets Satellite Images -- An Evaluation on Handcrafted and Learning-based Features for Multi-date Satellite Stereo Images
Shuang Song, Luca Morelli, Xinyi Wu +3
A critical step in the digital surface models(DSM) generation is feature matching. Off-track (or multi-date) satellite stereo images, in particular, can challenge the performance o…
Geometry-Aware Feature Matching for Large-Scale Structure from Motion
Gonglin Chen, Jinsen Wu, Haiwei Chen +5
Establishing consistent and dense correspondences across multiple images is crucial for Structure from Motion (SfM) systems. Significant view changes, such as air-to-ground with ve…
Geospecific View Generation -- Geometry-Context Aware High-resolution Ground View Inference from Satellite Views
Ningli Xu, Rongjun Qin
Predicting realistic ground views from satellite imagery in urban scenes is a challenging task due to the significant view gaps between satellite and ground-view images. We propose…
Large-scale DSM registration via motion averaging
Ningli Xu, Rongjun Qin
Generating wide-area digital surface models (DSMs) requires registering a large number of individual, and partially overlapped DSMs. This presents a challenging problem for a typic…
UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation
Pengfei Zhang, Chang Li, Yongjun Zhang +3
Remotely sensed imagery interpretation (RSII) faces the three major problems: (1) objective representation of spatial distribution patterns; (2) edge uncertainty problem caused by…
Image Fusion in Remote Sensing: An Overview and Meta Analysis
Hessah Albanwan, Rongjun Qin, Yang Tang
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, an…
On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data
Rongjun Qin, Guixiang Zhang, Yang Tang
Recent deep learning-based methods outperform traditional learning methods on remote sensing (RS) semantic segmentation/classification tasks. However, they require large training d…
A Critical Analysis of Internal Reliability for Uncertainty Quantification of Dense Image Matching in Multi-view Stereo
Debao Huang, Rongjun Qin
Nowadays, photogrammetrically derived point clouds are widely used in many civilian applications due to their low cost and flexibility in acquisition. Typically, photogrammetric po…