ACADSTAFF UGM

CREATION
Title : MCCNet: Multi-Color Cascade Network with Weight Transfer for Single Image Depth Prediction on Outdoor Relief Images
Author :

Dr. techn. Aufaclav Zatu Kusuma Frisky S.Si., M.Sc. (1) Andi Putranto, S.S., M.Sc. (2) Sebastian Zambanini (3) Robert Sablatnig (4)
Date : 21 2021
Keyword : Multi-color,Cascade,Single image,Depth prediction,Cultural heritage,Relief ,Weight transfer Multi-color,Cascade,Single image,Depth prediction,Cultural heritage,Relief ,Weight transfer
Abstract : Single image depth prediction is considerably difficult since depth cannot be estimated from pixel correspondences. Thus, prior knowledge, such as registered pixel and depth information from the user is required. Another problem rises when targeting a specific domain requirement as the number of freely available training datasets is limited. Due to color problem in relief images, we present a new outdoor Registered Relief Depth (RRD) Prambanan dataset, consisting of outdoor images of Prambanan temple relief with registered depth information supervised by archaeologists and computer scientists. In order to solve the problem, we also propose a new depth predictor, called Multi-Color Cascade Network (MCCNet), with weight transfer. Applied on the new RRD Prambanan dataset, our method performs better in different materials than the baseline with 2.53 mm RMSE. In the NYU Depth V2 dataset, our method’s performance is better than the baselines and in line with other state-of-the-art works.
Group of Knowledge :
Level : Internasional
Status :
Published
Document
No Title Document Type Action
1 L1 Penghargaan Karya Ilmiah Telah Terbit 2021.pdf
Document Type : Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian)
Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian) View
2 Patrech_compressed.pdf
Document Type : [PAK] Full Dokumen
[PAK] Full Dokumen View
3 MCCNet Multi-Color Cascade Network with Weight Transfer for Single Image Depth Prediction on Outdoor Relief Images.pdf
Document Type : Cek Similarity
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