Robust Geometry-Preserving Depth Estimation Using Differentiable
  Rendering
Chi Zhang Wei Yin Gang Yu Zhibin Wang Tao Chen Bin Fu Joey Tianyi Zhou Chunhua Shen
In this study, we address the challenge of 3D scene structure recovery from
monocular depth estimation. While traditional depth estimation methods leverage
labeled datasets to directly predict absolute depth, recent advancements
advocate for mix-dataset training, enhancing generalization across diverse
scenes. However, such mixed dataset training yields depth predictions only up
to an unknown scale and shift, hindering accurate 3D reconstructions. Existing
solutions necessitate extra 3D datasets or geometry-complete depth annotations,
constraints that limit their versatility. In this paper, we propose a learning
framework that trains models to predict geometry-preserving depth without
requiring extra data or annotations. To produce realistic 3D structures, we
render novel views of the reconstructed scenes and design loss functions to
promote depth estimation consistency across different views. Comprehensive
experiments underscore our framework's superior generalization capabilities,
surpassing existing state-of-the-art methods on several benchmark datasets
without leveraging extra training information. Moreover, our innovative loss
functions empower the model to autonomously recover domain-specific
scale-and-shift coefficients using solely unlabeled images.