Layered Depth Refinement with Mask Guidance

TL;DR

This paper introduces a mask-guided layered depth refinement method that improves depth map accuracy by leveraging high-quality masks and a self-supervised learning scheme, addressing common depth prediction errors.

Contribution

The paper proposes a novel mask-guided depth refinement framework with layered inpainting/outpainting and a self-supervised scheme for training without paired datasets.

Findings

Robustly refines depth maps using arbitrary masks and initial predictions.

Accurately improves depth boundaries and fills holes in depth maps.

Demonstrates effectiveness on real-world applications.

Abstract

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/ .