42-Issue 1

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https://diglib.eg.org/handle/10.2312/2633291

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Browsing 42-Issue 1 by Subject "image and video processing"

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    Novel View Synthesis Of Transparent Object From a Single Image
    (Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Zhou, Shizhe; Wang, Zezu; Ye, Dongwei; Hauser, Helwig and Alliez, Pierre
    We propose a method for converting a single image of a transparent object into multi‐view photo that enables users observing the object from multiple new angles, without inputting any 3D shape. The complex light paths formed by refraction and reflection makes it challenging to compute the lighting effects of transparent objects from a new angle. We construct an encoder–decoder network for normal reconstruction and texture extraction, which enables synthesizing novel views of transparent object from a set of new views and new environment maps using only one RGB image. By simultaneously considering the optical transmission and perspective variation, our network learns the characteristics of optical transmission and the change of perspective as guidance to the conversion from RGB colours to surface normals. A texture extraction subnetwork is proposed to alleviate the contour loss phenomenon during normal map generation. We test our method using 3D objects within and without our training data, including real 3D objects that exists in our lab, and completely new environment maps that we take using our phones. The results show that our method performs better on view synthesis of transparent objects in complex scenes using only a single‐view image.
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    Test‐Time Optimization for Video Depth Estimation Using Pseudo Reference Depth
    (Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Zeng, Libing; Kalantari, Nima Khademi; Hauser, Helwig and Alliez, Pierre
    In this paper, we propose a learning‐based test‐time optimization approach for reconstructing geometrically consistent depth maps from a monocular video. Specifically, we optimize an existing single image depth estimation network on the test example at hand. We do so by introducing pseudo reference depth maps which are computed based on the observation that the optical flow displacement for an image pair should be consistent with the displacement obtained by depth‐reprojection. Additionally, we discard inaccurate pseudo reference depth maps using a simple median strategy and propose a way to compute a confidence map for the reference depth. We use our pseudo reference depth and the confidence map to formulate a loss function for performing the test‐time optimization in an efficient and effective manner. We compare our approach against the state‐of‐the‐art methods on various scenes both visually and numerically. Our approach is on average 2.5× faster than the state of the art and produces depth maps with higher quality.