Related papers: TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network

TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network

URL: http://arxiv.org/abs/2208.08768v2
Date: Mon, 22 Aug 2022 14:45:12 GMT
Title: TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network
Authors: Ahmet Serdar Karadeniz, Sk Aziz Ali, Anis Kacem, Elona Dupont, Djamila Aouada
Abstract summary: Reconstructing 3D human body shapes from 3D partial textured scans is a fundamental task for many computer vision and graphics applications. We propose a new neural network architecture for 3D body shape and high-resolution texture completion.
Score: 14.389603490486364
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Reconstructing 3D human body shapes from 3D partial textured scans remains a fundamental task for many computer vision and graphics applications -- e.g., body animation, and virtual dressing. We propose a new neural network architecture for 3D body shape and high-resolution texture completion -- BCom-Net -- that can reconstruct the full geometry from mid-level to high-level partial input scans. We decompose the overall reconstruction task into two stages - first, a joint implicit learning network (SCom-Net and TCom-Net) that takes a voxelized scan and its occupancy grid as input to reconstruct the full body shape and predict vertex textures. Second, a high-resolution texture completion network, that utilizes the predicted coarse vertex textures to inpaint the missing parts of the partial 'texture atlas'. A thorough experimental evaluation on 3DBodyTex.V2 dataset shows that our method achieves competitive results with respect to the state-of-the-art while generalizing to different types and levels of partial shapes. The proposed method has also ranked second in the track1 of SHApe Recovery from Partial textured 3D scans (SHARP [38,1]) 2022 challenge1.

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