Self-Guided Instance-Aware Network for Depth Completion and Enhancement

by   Zhongzhen Luo, et al.

Depth completion aims at inferring a dense depth image from sparse depth measurement since glossy, transparent or distant surface cannot be scanned properly by the sensor. Most of existing methods directly interpolate the missing depth measurements based on pixel-wise image content and the corresponding neighboring depth values. Consequently, this leads to blurred boundaries or inaccurate structure of object. To address these problems, we propose a novel self-guided instance-aware network (SG-IANet) that: (1) utilize self-guided mechanism to extract instance-level features that is needed for depth restoration, (2) exploit the geometric and context information into network learning to conform to the underlying constraints for edge clarity and structure consistency, (3) regularize the depth estimation and mitigate the impact of noise by instance-aware learning, and (4) train with synthetic data only by domain randomization to bridge the reality gap. Extensive experiments on synthetic and real world dataset demonstrate that our proposed method outperforms previous works. Further ablation studies give more insights into the proposed method and demonstrate the generalization capability of our model.



There are no comments yet.


page 1

page 3

page 4

page 5

page 6


Indoor Depth Completion with Boundary Consistency and Self-Attention

Depth estimation features are helpful for 3D recognition. Commodity-grad...

Depth Completion using Geometry-Aware Embedding

Exploiting internal spatial geometric constraints of sparse LiDARs is be...

Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints

Depth completion aims to recover dense depth maps from sparse depth meas...

Deep Depth Completion of a Single RGB-D Image

The goal of our work is to complete the depth channel of an RGB-D image....

Semantic-Guided Representation Enhancement for Self-supervised Monocular Trained Depth Estimation

Self-supervised depth estimation has shown its great effectiveness in pr...

Depth Completion via Deep Basis Fitting

In this paper we consider the task of image-guided depth completion wher...

CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion

Depth Completion deals with the problem of converting a sparse depth map...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.