In this paper, we propose a 3D Digital Surface Model (DSM) reconstruction method from uncalibrated Multi-view Satellite Stereo (MVSS) images, where Rational Polynomial Coefficient (RPC) sensor parameters are not available. While recent investigations have introduced several techniques to reconstruct high-precision and high-density DSMs from MVSS images, they inherently depend on the use of geo-corrected RPC sensor parameters. However, RPC parameters from satellite sensors are subject to being erroneous due to inaccurate sensor data. In addition, due to the increasing data availability from the internet, uncalibrated satellite images can be easily obtained without RPC parameters. This study proposes a novel method to reconstruct a 3D DSM from uncalibrated MVSS images by estimating and integrating RPC parameters. To do this, we first employ a structure from motion (SfM) and 3D homography-based geo-referencing method to reconstruct an initial DSM. Second, we sample 3D points from the initial DSM as references and reproject them to the 2D image space to determine 3D–2D correspondences. Using the correspondences, we directly calculate all RPC parameters. To overcome the memory shortage problem while running the large size of satellite images, we also propose an RPC integration method. Image space is partitioned to multiple tiles, and RPC estimation is performed independently in each tile. Then, all tiles’ RPCs are integrated into the final RPC to represent the geometry of the whole image space. Finally, the integrated RPC is used to run a true MVSS pipeline to obtain the 3D DSM. The experimental results show that the proposed method can achieve 1.455 m Mean Absolute Error (MAE) in the height map reconstruction from multi-view satellite benchmark datasets. We also show that the proposed method can be used to reconstruct a geo-referenced 3D DSM from uncalibrated and freely available Google Earth imagery.

In this paper, we propose a method for performing 3D reconstruction by generating virtual RPC parameters from multi-view satellite stereo images provided by Google Earth (GE) software. In the multi-view stereo (MVS) image in a general case, after the pose and parameters of the camera are estimated, a dense 3D surface can be reconstructed. However, in the case of satellite images, it is not easy to obtain the original images with pose parameters of an area of interest. In the case of GE software, which can obtain images across the globe, the images provided are georeferenced and modified to fit the ground control point (GCP), so there is no camera model to explain the projection relationship. Therefore, the purpose of the proposed method is to perform 3D reconstruction by generating virtual camera parameters in modified satellite images obtained from GE software. In the proposed method, satellite images obtained from GE are estimated to be pinhole images using structure from motion (SfM) for initial reconstruction. After initial reconstruction, the 3D model is transformed from a distorted hexahedral space formed along a pixel ray to a UTM coordinate system metric space through a 3D homography-based georeferencing. A virtual rational polynomial camera (RPC) parameter is calculated through the satellite images and the 3D interspace correspondence point of UTM coordinates. The result is generated by virtual RPC and the MVS method using the RPC model. The reconstructed DSM using virtual RPC is improved over the initial reconstruction of the proposed process, and error measurement in the area with GT obtained significant results with an average of 1.366m on an MAE method.

Robust estimation of camera trajectories from unconstrained image sequences remains a fundamental problem in computer vision and robotics. Recently, a diffusion-based camera tracking network has shown strong performance in sparse-view and single-object-centric settings, where a consistent object is observed across frames. However, when multiple objects appear sequentially in a video, the initially observed object may disappear as the sequence progresses, which prevents maintaining the “single-object-centric” paradigm across all frames and degrades pose estimation when the conventional method is applied to the multi-object sequence. In this work, we propose an object-centric camera pose estimation framework that handles such sequences by partitioning a video into object-level sub-scenes. As a baseline network, Ray Diffusion is applied to single-object sub-scenes, while frame-to-frame camera motion in multi-object sub-scenes is estimated using monocular video depth, object masks, and point cloud alignment using Iterative Closest Point (ICP). Since the domain of pose estimation from different sub-scenes is inconsistent in terms of pose scale, it requires seamless concatenation of pose estimation results through all sub-scenes. In this regard, we introduce a scale alignment strategy based on reprojection error minimization. This enables the pose estimates from individual sub-scenes to be integrated into a single and seamless camera trajectory. We evaluate the proposed method on a newly collected indoor dataset consisting of 40 multi-object video sequences. Experimental results compare our camera trajectory estimation with both the diffusion-based method and the state-of-the-art visual SLAM methods.

Propeller cavitation induces severe pressure fluctuations, underwater radiated noise, vibration, and structural damage to both the propeller and the hull. Precise evaluation of cavitation dynamics during model-scale testing is thus critical for predicting full-scale vessel performance and optimizing propeller geometry. Although recent advances have leveraged 3D computer vision for cavity volume measurement, continuous and inconsistent vibration in cavitation tunnel often invalidates calibration accuracy, degrading overall measurement precision. To address this challenge, we present a robust stereo vision framework specifically tailored for 3D cavitation quantification in high-vibration environments. Utilizing the propeller CAD model as a reference, the proposed framework employs an iterative optimization algorithm to dynamically compensate for vibration-induced extrinsic parameter drift, subsequently reconstructing the 3D cavity volume via stereo matching. The performance of the framework was evaluated by using two types of propeller. The results demonstrate high fidelity, achieving a mean absolute error (MAE) of 2.36 mm and a relative distance error of 0.12% against the reference CAD model. Furthermore, the proposed method yields a 21.3% improvement in reconstruction accuracy over conventional static stereo vision systems.