UniRender: Reconstructing 3D Surfaces from Aerial Images with a Unified Rendering Scheme

While recent advances in the field of neural rendering have shown impressive 3D reconstruction performance, it is still a challenge to accurately capture the appearance and geometry of a scene by using neural rendering, especially for remote sensing scenes. This is because both rendering methods, i.e., surface rendering and volume rendering, have their own limitations. Furthermore, when neural rendering is applied to remote sensing scenes, the view sparsity and content complexity that characterize these scenes will severely hinder its performance. In this work, we aim to address these challenges and to make neural rendering techniques available for 3D reconstruction in remote sensing environments. To achieve this, we propose a novel 3D surface reconstruction method called UniRender. UniRender offers three improvements in locating an accurate 3D surface by using neural rendering: (1) unifying surface and volume rendering by employing their strengths while discarding their weaknesses, which enables accurate 3D surface position localization in a coarse-to-fine manner; (2) incorporating photometric consistency constraints during rendering, and utilizing the points reconstructed by structure from motion (SFM) or multi-view stereo (MVS), to constrain reconstructed surfaces, which significantly improves the accuracy of 3D reconstruction; (3) improving the sampling strategy by locating sampling points in the foreground regions where the surface needs to be reconstructed, thus obtaining better detail in the reconstruction results. Extensive experiments demonstrate that UniRender can reconstruct high-quality 3D surfaces in various remote sensing scenes.