Enhancing Depth Completion with Multi-View Monitored Distillation

03/28/2023
by   Jia-Wei Guo, et al.
0

This paper presents a novel method for depth completion, which leverages multi-view improved monitored distillation to generate more precise depth maps. Our approach builds upon the state-of-the-art ensemble distillation method, in which we introduce a stereo-based model as a teacher model to improve the accuracy of the student model for depth completion. By minimizing the reconstruction error for a given image during ensemble distillation, we can avoid learning inherent error modes of completion-based teachers. To provide self-supervised information, we also employ multi-view depth consistency and multi-scale minimum reprojection. These techniques utilize existing structural constraints to yield supervised signals for student model training, without requiring costly ground truth depth information. Our extensive experimental evaluation demonstrates that our proposed method significantly improves the accuracy of the baseline monitored distillation method.

READ FULL TEXT

page 2

page 3

page 5

07/21/2022

KD-MVS: Knowledge Distillation Based Self-supervised Learning for MVS

Supervised multi-view stereo (MVS) methods have achieved remarkable prog...
03/30/2022

Monitored Distillation for Positive Congruent Depth Completion

We propose a method to infer a dense depth map from a single image, its ...
12/21/2019

Depth Completion via Deep Basis Fitting

In this paper we consider the task of image-guided depth completion wher...
11/15/2020

Online Ensemble Model Compression using Knowledge Distillation

This paper presents a novel knowledge distillation based model compressi...
02/20/2023

Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset Refinement

Monocular depth estimation plays a fundamental role in computer vision. ...
08/09/2023

Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR

The application of 3D ground-penetrating radar (3D-GPR) for subgrade dis...

Please sign up or login with your details

Forgot password? Click here to reset