Wasserstein Generative Adversarial Network for Depth Completion With Anisotropic Diffusion Depth Enhancement

Wasserstein Generative Adversarial Network for Depth Completion With Anisotropic Diffusion Depth Enhancement

The objective of depth completion is to generate a dense depth map by upsampling a sparse one. However, irregular sparse patterns or the lack of groundtruth data caused by unstructured data make depth completion extremely challenging. Sensor fusion using both RGB and LIDAR sensors can help produce a...

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Bibliographic Details
Main Authors: Tri Minh Nguyen, Myungsik Yoo
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Depth completion
LIDAR sparse depth
anisotropic diffusion
generative adversarial network
Wasserstein GAN
Online Access:https://ieeexplore.ieee.org/document/9680719/

Internet

https://ieeexplore.ieee.org/document/9680719/

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