Automatic Spatial Template Generation for Realistic 3D Modeling of Large-Scale Indoor Spaces

Hyeon, Janghun; Choi, Hong Soo; Kim, Joohyung; Jang, Bumchul; Kang, Jaehyeon; Doh, Nakju Lett

doi:10.1109/iros40897.2019.8968286

Cited by 7 publications

(8 citation statements)

References 15 publications

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“…We evaluate our multiview inpainting pipeline on real indoor spaces introduced in recent works. 1,9 The properties of each space are summarized in Table 1. The degree of light difference, occlusions, or environmental complexity of a space is indicated qualitatively by "þ" sign, where more "þ" signs represent harder conditions for multiview inpainting (Experiencing real-time rendering using our multiview inpainting results is available at www.teevr.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Figure 2). Global guidelines are obtained by automatic algorithm, 9 which are overlaid with user guidelines. Masks were made manually for the purpose of thorough multiview inpainting.…”

Section: Initial Datamentioning

confidence: 99%

“…Recent works overcome Manhattan assumption for indoor modeling or integrate the 3D model with IBR. 9,14 Hedman et al 2 proposed a real-time IBR method. However, this method has limitations in rendering larger than room-scale spaces.…”

Section: Indoor Modeling and Ibrmentioning

confidence: 99%

“…Turner et al 15 reconstruct an architectural mesh but do not remove objects in the images, which results in flattened object textures on the architectural mesh. In addition, recent studies 1,9 have focused on architectural component modeling and objects removal in images. However, the object removal in an image might cause a discordance of colors between multiple images.…”

Section: Indoor Modeling and Ibrmentioning

confidence: 99%

“…For an experiment validation, we utilized real indoor data sets 1,9 enabling a real-time rendering of large-scale indoor spaces. For the color consistency, we developed new measures to evaluate intensity-free color consistency among multiple inpainted images, as well as the similarity between multiple inpainted images and images without occlusions.…”

Section: Introductionmentioning

confidence: 99%

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Generative multiview inpainting for object removal in large indoor spaces

Kim

Hyeon

Doh

2021

International Journal of Advanced Robotic Systems

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As interest in image-based rendering increases, the need for multiview inpainting is emerging. Despite of rapid progresses in single-image inpainting based on deep learning approaches, they have no constraint in obtaining color consistency over multiple inpainted images. We target object removal in large-scale indoor spaces and propose a novel pipeline of multiview inpainting to achieve color consistency and boundary consistency in multiple images. The first step of the pipeline is to create color prior information on masks by coloring point clouds from multiple images and projecting the colored point clouds onto the image planes. Next, a generative inpainting network accepts a masked image, a color prior image, imperfect guideline, and two different masks as inputs and yields the refined guideline and inpainted image as outputs. The color prior and guideline input ensure color and boundary consistencies across multiple images. We validate our pipeline on real indoor data sets quantitatively using consistency distance and similarity distance, metrics we defined for comparing results of multiview inpainting and qualitatively.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…Figure 2). Global guidelines are obtained by automatic algorithm, 9 which are overlaid with user guidelines. Masks were made manually for the purpose of thorough multiview inpainting.…”

Section: Initial Datamentioning

confidence: 99%