Layered Depth Refinement with Mask Guidance
This work addresses depth refinement for applications like 3D rendering and image effects, offering a practical solution using readily available masks, but it is incremental as it builds on existing depth estimation methods.
The paper tackles the problem of inaccurate depth maps from single image depth estimation models, particularly in boundary regions and isolated holes, by proposing a mask-guided depth refinement framework that uses arbitrary masks to decompose and refine depth layers, achieving robust improvements across different mask types and initial predictions.
Depth maps are used in a wide range of applications from 3D rendering to 2D image effects such as Bokeh. However, those predicted by single image depth estimation (SIDE) models often fail to capture isolated holes in objects and/or have inaccurate boundary regions. Meanwhile, high-quality masks are much easier to obtain, using commercial auto-masking tools or off-the-shelf methods of segmentation and matting or even by manual editing. Hence, in this paper, we formulate a novel problem of mask-guided depth refinement that utilizes a generic mask to refine the depth prediction of SIDE models. Our framework performs layered refinement and inpainting/outpainting, decomposing the depth map into two separate layers signified by the mask and the inverse mask. As datasets with both depth and mask annotations are scarce, we propose a self-supervised learning scheme that uses arbitrary masks and RGB-D datasets. We empirically show that our method is robust to different types of masks and initial depth predictions, accurately refining depth values in inner and outer mask boundary regions. We further analyze our model with an ablation study and demonstrate results on real applications. More information can be found at https://sooyekim.github.io/MaskDepth/ .