Recent developments in 3D Gaussian Splatting have made significant advances in surface reconstruction. However, scaling these methods to large-scale scenes remains challenging due to high computational demands and the complex dynamic appearances typical of outdoor environments. These challenges hinder the application in aerial surveying and autonomous driving. This paper proposes a novel solution to reconstruct large-scale surfaces with fine details, supervised by full-sized images. Firstly, we introduce a coarse-to-fine strategy to reconstruct a coarse model efficiently, followed by adaptive scene partitioning and sub-scene refining from image segments. Additionally, we integrate a decoupling appearance model to capture global appearance variations and a transient mask model to mitigate interference from moving objects. Finally, we expand the multi-view constraint and introduce a single-view regularization for texture-less areas. Our experiments were conducted on the publicly available dataset GauU-Scene V2, which was captured using unmanned aerial vehicles. To the best of our knowledge, our method outperforms existing NeRF-based and Gaussian-based methods, achieving high-fidelity visual results and accurate surface from full-size image optimization. Open-source code will be available on GitHub.

High-resolution images of the Martian surface, collected by cameras onboard rovers, offer unique insights unavailable from satellite images and are crucial for rover navigation and geological study on the Martian surface. However, the large variations in spatial resolution and viewpoint across images acquired from different rover stations, exacerbated by the textureless nature of the Martian surface, pose significant challenges for effective 3D surface reconstruction, a fundamental task in planetary topographic mapping. Thus, this paper proposes a deep learning-based approach to enable robust image matching constructed from multi-level semantic cues, for large-scale 3D reconstruction from rover images. First, a Siamese transformer-based neural network is used to perform semantic segmentation of the rover images, thereby extracting multi-level semantic cues. Second, feature matching is performed using rover images collected from different stations, in which these semantic cues are integrated to enhance feature descriptor construction, contextual aggregation, and outlier removal, yielding robust cross-station matches. These matches facilitate the bundle adjustment to link cross-station rover images accurately. Third, in the dense matching of rover images, frequency-domain matching is proposed and embedded with semantic cues to improve matching reliability and preserve surface discontinuities. Lastly, the disparity maps generated from the matching results are used to derive the 3D point clouds, which are then meshed to generate 3D surface models. Experiments are conducted on two image datasets of typical Martian scenes collected by the Zhurong rover to evaluate the performance of the proposed method. The results indicate that image residuals of around 1.5 pixels on average are achieved for the bundle adjustment of cross-station images using the matched feature points, and the final generated 3D models exhibit an accuracy better than 0.5 m. Compared with the cutting-edge commercial software, the generated 3D models from our method exhibit superior quality in terms of both accuracy and coverage, highlighting the effectiveness of the semantic-aware image matching algorithm.

In the field of clinical chronic diseases, common prediction results (such as survival rate) and effect size hazard ratio (HR) are relative indicators, resulting in more abstract information. However, clinicians and patients are more interested in simple and intuitive concepts of (survival) time, such as how long a patient may live or how much longer a patient in a treatment group will live. In addition, due to the long follow-up time, resulting in generation of longitudinal time-dependent covariate information, patients are interested in how long they will survive at each follow-up visit. In this study, based on a time scale indicator-restricted mean survival time (RMST)-we proposed a dynamic RMST prediction model by considering longitudinal time-dependent covariates and utilizing joint model techniques. The model can describe the change trajectory of longitudinal time-dependent covariates and predict the average survival times of patients at different time points (such as follow-up visits). Simulation studies through Monte Carlo cross-validation showed that the dynamic RMST prediction model was superior to the static RMST model. In addition, the dynamic RMST prediction model was applied to a primary biliary cirrhosis (PBC) population to dynamically predict the average survival times of the patients, and the average C-index of the internal validation of the model reached 0.81, which was better than that of the static RMST regression. Therefore, the proposed dynamic RMST prediction model has better performance in prediction and can provide a scientific basis for clinicians and patients to make clinical decisions.

We study a realistic Bi-objective Multimodal Transportation Planning Problem (BMTPP) faced by logistics companies when trying to obtain cost advantages and improve the customer satisfaction in a competitive market. The two objectives considered are: the minimization of total transportation cost and the maximization of service quality. Given a set of transportation orders described by an origin, a destination and a time window, solving BMTPP involves determining the delivery path for each order in a capacitated network as well as selecting the carrier with the best service quality for each edge of the path. The BMTPP is formulated as a novel bi-objective mixed integer linear programming model and an iterative$\epsilon $-constraint method is applied to solve it. As the NP-hardness of the single-objective problems derived from BMTPP, a Lagrangian Relaxation (LR) heuristic which can not only provide a near-optimal solution but also a lower bound for each of the single-objective problems is developed. 100 randomly generated instances are tested and the computational results demonstrate the effectiveness of the heuristic in obtaining a tight lower bound and a high-quality near-optimal solution for the derived single-objective problem. Various performance indicators show the high-quality of the Pareto front of the bi-objective problem obtained by the heuristic. We also provide a case study for the proposed LR heuristic in a logistics network in China.

Images from the high-resolution stereo camera (HRSC) is an important data source for retrieving 3-D information of the Martian surface. Photogrammetry has been widely applied to HRSC images to generate digital elevation models (DEMs). However, in circumstances of insufficient textures or severe atmospheric influences on Mars, photogrammetry may fail to generate DEMs of favorable quality. Besides, photogrammetry is not able to recover pixelwise geometrical information. Meanwhile, photoclinometry is a promising method to retrieve pixelwise 3-D information from image intensities. Few works in the past have explored the appropriate use of photoclinometry for HRSC data. This article presents an innovative approach of integrating photogrammetry and photoclinometry for pixelwise 3-D mapping of the Martian surface using multiple HRSC images. First, an effective photogrammetric strategy is developed to process multiple HRSC images. For images influenced by haze or aerosol problems, an automatic object-based matching algorithm is formulated to compensate for obvious flaws on the disparity map. Second, multiview photoclinometry considering atmospheric effect is developed to refine the photogrammetric DEM to pixel resolution. Qualitative and quantitative validations are conducted using two representative sets of HRSC images and compared with the reference data. The results show that the generated DEMs have good geometric consistency with the reference data, but offer subtle topographic details conforming to the original images. The proposed method provides a promising solution to exploit the HRSC images for 3-D mapping of the Martian surface with better resolution and coverage, which is of significance for future Mars exploration missions and scientific research.

The High-Resolution Imaging Camera (HiRIC) onboard China’s Tianwen-1 Mars probe aims to acquire detailed imagery of the Martian surface to comprehensively investigate its topography and geomorphology. The HiRIC is a pushbroom camera comprising three CCDs to simultaneously achieve sub-meter resolution and a large swath. However, processing HiRIC images using the conventional photogrammetric workflow is difficult due to the large shifts and narrow overlapping among the CCD lines. This paper presents a novel approach for photogrammetric processing of HiRIC images for precision topographic mapping that incorporates 1) the fitting of the initial Rational Polynomial Coefficients (RPCs) of images from the HiRIC position and pointing data, 2) a deep-learning-based method for tie-point matching between adjacent CCD images and cross-orbit images, 3) the bundle adjustment of multiple CCD images for tripled-epipolar image generation to ensure inner-orbit consistency, 4) the block adjustment of multiple orbit images to ensure cross-orbit consistency, and 5) dense image matching and space intersection based on the refined RPCs to generate Digital Elevation Models (DEMs). Experimental analyses were conducted using HiRIC images covering the landing region of the Zhurong rover. The results revealed that subpixel accuracy was achieved for image residuals among multiple-CCD or multiple-orbit images. Comparison with the reference data (HiRISE and MOLA DEMs) revealed a mean deviation of less than 7 m in terms of the geometric accuracy and the subtle topographic details of the HiRIC DEM. The presented approach offers a reliable solution for using the new dataset of HiRIC imagery for Mars topographic mapping.