FVMD-ISRe: 3-D Reconstruction From Few-View Multidate Satellite Images Based on the Implicit Surface Representation of Neural Radiance Fields

Three-dimensional reconstruction utilizing few-view satellite images can effectively reduce the cost of resources. However, methods that rely on dense stereo matching suffer severe performance degradation when confronted with differences of intersection angles and illumination in such non-standard stereo images, which are limited by the matching mechanism. Neural radiance fields (NeRFs) get rid of the matching restriction by utilizing the rendering pipeline, which has made significant progress in the synthesizing novel views and 3-D reconstruction. Nevertheless, when the available images are few-view and multidate, insufficient geometric constraints and illumination variations present great challenges in generating accurate 3-D models. In this article, we proposed a 3-D reconstruction framework called few-view multidate (FVMD)-implicit surface representation (ISRe), which is based on the ISRe of NeRF, for generating complete watertight mesh and digital surface model (DSM) from FVMD satellite images. We introduce the positional encoding with adaptive frequency module to extract finer geometric information from few-view images. Then, the solar information is incorporated into the rendering process, allowing the network to distinguish color changes arising from illumination differences in multidate images. Additionally, we employ some tricks like coordinate system optimization and network restructuring for enhancing network training. Advantages of FVMD-ISRe are demonstrated through both qualitative and quantitative experiments conducted on the US3D dataset. The results highlight our framework’s capacity to reconstruct accurate 3-D geometry, overcome the performance of traditional methods based on stereo matching and other NeRFs in various evaluation metrics. Code and data are available at
https://github.com/HEU-super-generalized-remote-sensing/FVMD-ISRe
.