Unsupervised Textured Terrain Generation via Differentiable Rendering

Peichi Zhou, Dingbo Lu, Chen Li, Jian Zhang, Long Liu, Changbo Wang

Abstract: Constructing large-scale realistic terrains using modern modeling tools is an extremely challenging task even for professional users, undermining the effectiveness of video games, virtual reality, and other applications. In this paper, we present a step towards unsupervised and realistic modeling of textured terrains from DEM and satellite imagery, built upon two-stage illumination and texture optimization via differentiable rendering. First, a differentiable renderer for satellite imagery is established based on the Lambert diffuse model that allows inverse optimization of material and lighting parameters towards specific objective. Second, the original illumination direction of satellite imagery is recovered by reducing the difference between the shadow distribution generated by the renderer and that of the satellite image in YCrCb colour space, leveraging the abundant geometric information of DEM. Third, we propose to generate the original texture of the shadowed region by introducing visual consistency and smoothness constraints via differentiable rendering to arrive at an end-to-end unsupervised architecture. Comprehensive experiments demonstrate the effectiveness and efficiency of our proposed method as a potential tool to achieve virtual terrain modeling for widespread graphics applications.