Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency

Abstract

Learning to reconstruct depths in a single image by watching unlabeled videos via deep convolutional network (DCN) is attracting significant attention in recent years. In this paper, we introduce a surface normal representation for unsupervised depth estimation framework. Our estimated depths are constrained to be compatible with predicted normals, yielding more robust geometry results. Specifically, we formulate an edge-aware depth-normal consistency term, and solve it by constructing a depth-to-normal layer and a normal-to-depth layer inside of the DCN. The depth-to-normal layer takes estimated depths as input, and computes normal directions using cross production based on neighboring pixels. Then given the estimated normals, the normal-to-depth layer outputs a regularized depth map through local planar smoothness. Both layers are computed with awareness of edges inside the image to help address the issue of depth/normal discontinuity and preserve sharp edges. Finally, to train the network, we apply the photometric error and gradient smoothness for both depth and normal predictions. We conducted experiments on both outdoor (KITTI) and indoor (NYUv2) datasets, and show that our algorithm vastly outperforms state of the art, which demonstrates the benefits from our approach.

Accepted at AAAI 2018

Cited by in corpus (15)

• Unsupervised Monocular Depth Learning in Dynamic Scenes
• Semantically-Guided Representation Learning for Self-Supervised Monocular Depth
• Monocular Depth Estimation with Self-supervised Instance Adaptation
• Feature-metric Loss for Self-supervised Learning of Depth and Egomotion
• VisualEchoes: Spatial Image Representation Learning through Echolocation
• Self-supervised Learning for Single View Depth and Surface Normal Estimation
• Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation
• Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark
• Improving Depth Estimation using Location Information
• Variational Monocular Depth Estimation for Reliability Prediction
• Radar-Camera Pixel Depth Association for Depth Completion
• Self-Guided Instance-Aware Network for Depth Completion and Enhancement
• PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation
• Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation
• A Dark Flash Normal Camera