Abstract. High-precision digital terrain models (DTMs) are important for Mars explorations and research, providing indispensable spatial information for landing site assessment, rover path planning, and surface environment analysis. However, challenges such as high-resolution stereo data scarcity and complex atmospheric conditions on the Martian surface result in traditional terrain reconstruction methods suffer from limitations in accuracy, coverage and resolution. To enhance the model’s ability to recover fine-grained topography, we present a diffusion-based monocular terrain reconstruction method, which progressively recovers Martian terrains from single-view high-resolution optical images. We employed a multi-scale U-Net denoising network with attention mechanisms and introduced an additional end-to-end depth constraint. To improve terrain reconstruction efficiency, we implemented a diffusion model in the latent space and adopted a skipping sampling mechanism. We employed the proposed method to reconstruct terrain in different regions. Experimental results demonstrate that the reconstructed terrain achieves an accuracy of 2 m. Furthermore, compared to photogrammetric terrain, the shaded relief generated by our method exhibits greater similarity to the input imagery.
Jiarui Cao, Rong Huang, Yusheng Xu et al.· The International Archives o...· 0 citations
Abstract. Integration of LROC NAC and ShadowCam imagery is essential for meter-scale controlled mapping of the entire lunar south pole including Permanently Shadowed Regions (PSRs), but remains challenging due to extreme radiometric differences, sparse overlap across illumination boundaries, and ill-conditioned bundle adjustment networks. This paper proposes a LOLA DEM-mediated multi-source bundle adjustment framework for controlled lunar polar mapping. A hierarchical cross-modality matching strategy is developed using first- and second-order Gaussian steerable gradient features with multi-scale fusion and phase-correlation-based subpixel refinement. Sensor-specific geometric models are established using second-order polynomial transformations for NAC orthoimages and rational polynomial models for ShadowCam map-projected images. Five types of geometric constraints are formulated to integrate intra-sensor, limited cross-sensor, and image-to-DEM observations, with the LOLA DEM acting as a common geometric mediator. To stabilize the heterogeneous network, a hybrid L1-L2 regularization model with adaptive two-stage weighting is optimized using ADMM algorithm. Experiments in the lunar south polar region demonstrate substantial improvements on intra-sensor, cross-sensor, and image-to-reference positioning accuracy. The final seamless 1 m/pixel orthorectified mosaics achieve approximately 5 m absolute accuracy, validating the proposed framework for geometrically unifying illuminated and permanently shadowed terrain in lunar polar controlled mapping.
Qionghua You, Zhen Ye, Yusheng Xu et al.· The International Archives o...· 0 citations
