Three-dimensional mapping of the lunar surface is of immense importance for lunar exploration missions and scientific research. Research in the past decades has mainly focused on laser altimetry and photogrammetry methods to generate precise DEM based on rigorous mathematical calculation. Due to the limitation of the hardware and algorithms, these methods cannot meet the requirements for exploration of the lunar south pole, which demands meter-level resolution to guarantee safe descending and traversing. Thus, photoclinometry has received much attention because it can make full use of the images to reconstruct pixel-wise-resolution DEMs from monocular images. However, the lunar south pole suffers from solar elevation angles of nearly 0 degree, and the shadow effect is severe such that effectively recovering all the terrain details using only a single image is not possible. Moreover, the loss functions established directly from multiple images in the photoclinometry algorithm may be inconsistent due to the major differences among the images caused by the varying illuminations. This limitation may cause difficulties in converging the loss and may prevent recovering the terrain. The paper presents a multiple image photoclinometry method for pixel-wise 3D reconstruction at the lunar south pole. The inputs of the method are multiple high-resolution images and corresponding low-resolution DEM, and a multi-resolution pyramid is used to optimize the DEM step by step. In terms of the loss function, this paper proposes a similarity constraint by fully considering the shadow differences between the lunar south pole images. Rather than optimizing a single DEM, the approach initializes multiple DEMs using the low-resolution DEM associated with each image. While the conventional photoclinometry constraints are established on each DEM, an encourage mechanism is then introduced to ensure the pixels cover the same region exhibit consistent elevation. With respect to the loss optimization, an adaptive learning rate optimizer is introduced to guarantee the convergence of the loss function. The gradient update information in the optimization stage could also provide a reference for the photometric quality of the image, which becomes an important index for the following DEM fusion. Two regions are selected around the Shackleton Crater at the lunar south pole for experimental analysis. Regarding the LDEM and photogrammetric DEM as the reference, the geometric accuracy of the DEMs generated from the proposed approach is about one pixel, and the terrain details (e.g., small craters and ridges) presented on the images are fully reconstructed. Rendering simulation is also conducted based on the reconstructed photoclinometry DEM for in-depth evaluation. By analyzing the DEM under various illumination conditions, the simulated images closely resemble the original NAC images, and thereby demonstrate the accuracy and precision of the photoclinometry-derived DEM. The method presented in this paper can effectively reconstruct high-resolution, high-precision DEMs from monocular images at the lunar south pole, and provide important 3D data support for future exploration missions to the lunar south pole and related scientific research.

Lunar South Pole;DEM;pixel-wise 3D reconstruction;photoclinometry;photogrammetry