FERN 发表于 2025-3-21 16:58:59
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http://reply.papertrans.cn/24/2343/234267/234267_2.pngPepsin 发表于 2025-3-22 01:54:28
Topics in Regulatory Economics and Policyposed network is composed of a shared encoder and three decoders, called G-Net, N-Net, and M-Net, which estimate gradient maps, a normalized depth map, and a metric depth map, respectively. M-Net learns to estimate metric depths more accurately using relative depth features extracted by G-Net and N-Etymology 发表于 2025-3-22 07:07:07
http://reply.papertrans.cn/24/2343/234267/234267_4.pnganniversary 发表于 2025-3-22 10:16:45
Price and Value of Audiovisual Products,methods often need to train a big sampling network to support sampling under different resolutions, which must generate sampled points with the costly maximum resolution even if only low-resolution points need to be sampled. In this work, we propose a novel resolution-free point clouds sampling netwprogestogen 发表于 2025-3-22 14:46:11
The Economics of the Euro-Currency Systeme prior information intrinsic to the NRS.M matrix factorization theory. It is shown that such priors reside in the factorized matrices, and quite surprisingly, existing methods generally disregard them. The paper’s main contribution is to put forward a simple, methodical, and practical method that cprogestogen 发表于 2025-3-22 20:37:00
The Economics of the Euro-Currency Systemuch as those in space or deep under water, acquiring real images, even unannotated, is virtually impossible. In this paper, we propose a method that can be trained solely on synthetic images, or optionally using a few additional real ones. Given a rough pose estimate obtained from a first network, i仪式 发表于 2025-3-23 00:29:08
International Banking Stability,s, and multi-view imagery. High-resolution models enable photo-realistic avatars but at the cost of requiring studio settings not available to end users. Our goal is to create avatars directly from raw images without relying on expensive studio setups and surface tracking. While a few such approacheButtress 发表于 2025-3-23 04:03:24
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The Economics of the Financial Crisiss the parameters of a low-dimensional statistical body model (.SMPL) from input images, we train an ensemble of per vertex neural fields network. The network predicts, in a distributed manner, the vertex descent direction towards the ground truth, based on neural features extracted at the current ve