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DTSTAMP:20240214T070246Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T092500
DTEND;TZID=Australia/Melbourne:20231214T094000
UID:siggraphasia_SIGGRAPH Asia 2023_sess148_papers_257@linklings.com
SUMMARY:TwinTex: Geometry-aware Texture Generation for Abstracted 3D Archi
 tectural Models
DESCRIPTION:Technical Papers, TOG\n\nWeidan Xiong, Hongqian Zhang, Botao P
 eng, Ziyu Hu, Yongli Wu, Jianwei Guo, and Hui Huang (Shenzhen University)\
 n\nCoarse architectural models are often generated at scales ranging from 
 individual buildings to scenes for downstream applications such as Digital
  Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abs
 tracted as twins from 3D dense reconstructions. However, these models typi
 cally lack realistic texture relative to the real building or scene, makin
 g them unsuitable for vivid display or direct reference. In this paper, we
  present TwinTex, the first automatic texture mapping framework to generat
 e a photo-realistic texture for a piece-wise planar proxy. Our method addr
 esses most challenges occurring in such twin texture generation. Specifica
 lly, for each primitive plane, we first select a small set of photos with 
 greedy heuristics considering photometric quality, perspective quality and
  facade texture completeness. Then, different levels of line features (LoL
 s) are extracted from the set of selected photos to generate guidance for 
 later steps. With LoLs, we employ optimization algorithms to align texture
  with geometry from local to global. Finally, we fine-tune a diffusion mod
 el with a multi-mask initialization component and a new dataset to inpaint
  the missing region. Experimental results on many buildings, indoor scenes
  and man-made objects of varying complexity demonstrate the generalization
  ability of our algorithm. Our approach surpasses state-of-the-art texture
  mapping methods in terms of high-fidelity quality and reaches a human-exp
 ert production level with much less effort.\n\nRegistration Category: Full
  Access\n\nSession Chair: Lin Lu (Shandong University)
URL:https://asia.siggraph.org/2023/full-program?id=papers_257&sess=sess148
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