Mesh12 Convolutional Occupancy Networks, ECCV’20 논문 링크 : https://www.cvlibs.net/publications/Peng2020ECCV.pdf1. IntroductionTowards this goal, we introduce Convolutional Occupancy Networks, a novel representation for accurate large-scale 3D reconstruction with continuous implicit representations (Fig. 1). We demonstrate that this representation not only preserves fine geometric details, but also enables the reconstruction of complex indoor sce.. 2023. 3. 26. Dynamic Plane Convolutional Occupancy Networks, WACV’21 논문 링크 : https://openaccess.thecvf.com/content/WACV2021/papers/Lionar_Dynamic_Plane_Convolutional_Occupancy_Networks_WACV_2021_paper.pdf1. IntroductionIn this work, we propose Dynamic Plane Convolutional Occupancy Networks, an implicit representation that enables accurate scene-level reconstruction from 3D point clouds. Instead of learning features on three pre-defined canonical planes as in [28].. 2023. 3. 26. Occupancy Networks: Learning 3D Reconstruction in Function Space, CVPR’19 논문 링크 : https://openaccess.thecvf.com/content_CVPR_2019/papers/Mescheder_Occupancy_Networks_Learning_3D_Reconstruction_in_Function_Space_CVPR_2019_paper.pdf1. IntroductionIn this paper, we propose a novel approach to 3D reconstruction based on directly learning the continuous 3D occupancy function (Fig. 1d). Instead of predicting a voxelized representation at a fixed resolution, we predict the c.. 2023. 3. 26. Deep Marching Cubes: Learning Explicit Surface Representations, CVPR’18 논문 링크 : https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8578406AbstractIn this paper, we investigate the problem of end-to-end 3D surface prediction. We first demonstrate that the marching cubes algorithm is not differentiable and propose an alternative differentiable formulation which we insert as a final layer into a 3D convolutional neural network. We further propose a set of loss fu.. 2023. 3. 26. 이전 1 2 3 다음
