SurGe: Improved Surface Geometry in Point Maps

With impressive progress in architectures, training recipes, and quantity of high-quality data, recent monocular geometry estimation methods estimate global 3D geometry remarkably well. However, they still exhibit surprisingly low quality on surface details, which is clearly visible in qualitative predictions but not reflected in common metrics. To improve this shortcoming, we first formulate a metric that is capable of measuring the surface quality of the estimated geometry via normals derived from the predicted point map. Furthermore, we propose two manners to improve fine-grained local geometry in monocular geometry estimation models: (i) we present an improved point-based gradient loss, and (ii) we introduce a novel decoder based on neighborhood attention. We evaluate our proposed method on eight common zero-shot monocular geometry estimation benchmarks and achieve state-of-the-art results, particularly improving local surface geometry. Extensive ablations validate our design choices. Code and models will be open-sourced.