{"id":27118,"date":"2024-02-29T04:19:26","date_gmt":"2024-02-29T05:19:26","guid":{"rendered":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-%d9%be%db%8c%d8%b4%d8%a8%db%8c%d9%86%db%8c%da%a9%d9%86%d9%86%d8%af%d9%87-%d8%ba%db%8c%d8%b1%d8%ae%d8%b7%db%8c-%d8%a2%d9%85%d9%88%d8%ae%d8%aa%d9%87\/"},"modified":"2024-02-29T04:19:32","modified_gmt":"2024-02-29T05:19:32","slug":"%d9%85%d9%82%d8%a7%d9%84%d9%87-%d9%be%db%8c%d8%b4%d8%a8%db%8c%d9%86%db%8c%da%a9%d9%86%d9%86%d8%af%d9%87-%d8%ba%db%8c%d8%b1%d8%ae%d8%b7%db%8c-%d8%a2%d9%85%d9%88%d8%ae%d8%aa%d9%87","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-%d9%be%db%8c%d8%b4%d8%a8%db%8c%d9%86%db%8c%da%a9%d9%86%d9%86%d8%af%d9%87-%d8%ba%db%8c%d8%b1%d8%ae%d8%b7%db%8c-%d8%a2%d9%85%d9%88%d8%ae%d8%aa%d9%87\/","title":{"rendered":"\u0645\u0642\u0627\u0644\u0647 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u06a9\u0646\u0646\u062f\u0647 \u063a\u06cc\u0631\u062e\u0637\u06cc \u0622\u0645\u0648\u062e\u062a\u0647\u200c\u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u0641\u0634\u0631\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u0627\u0628\u0631 \u0646\u0642\u0637\u0647 \u0633\u0647\u200c\u0628\u0639\u062f\u06cc \u0646\u0645\u0648\u0646\u0647\u200c\u06af\u06cc\u0631\u06cc \u0627\u0646\u062a\u0642\u0627\u062f\u06cc"},"content":{"rendered":"<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\n<td>Learned Nonlinear Predictor for Critically Sampled 3D Point Cloud Attribute Compression<\/td>\n<\/tr>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n<td>\u0645\u0642\u0627\u0644\u0647 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u06a9\u0646\u0646\u062f\u0647 \u063a\u06cc\u0631\u062e\u0637\u06cc \u0622\u0645\u0648\u062e\u062a\u0647\u200c\u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u0641\u0634\u0631\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u0627\u0628\u0631 \u0646\u0642\u0637\u0647 \u0627\u06cc \u0633\u0647\u200c\u0628\u0639\u062f\u06cc \u0646\u0645\u0648\u0646\u0647\u200c\u06af\u06cc\u0631\u06cc \u0634\u062f\u0647 \u0627\u0646\u062a\u0642\u0627\u062f\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\n<td>Tam Thuc Do, Philip A. Chou, Gene Cheung<\/td>\n<\/tr>\n<tr>\n<td>\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 <\/td>\n<td>\u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647: <\/td>\n<td>PDF<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n<td>0<\/td>\n<\/tr>\n<tr>\n<td>\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a  <\/td>\n<td>Image and Video Processing,Machine Learning,Signal Processing,\u067e\u0631\u062f\u0627\u0632\u0634 \u062a\u0635\u0648\u06cc\u0631 \u0648 \u0641\u06cc\u0644\u0645 , \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 , \u067e\u0631\u062f\u0627\u0632\u0634 \u0633\u06cc\u06af\u0646\u0627\u0644 ,<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a    <\/td>\n<td>Submitted 22 November, 2023; originally announced November 2023.<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc    <\/td>\n<td>\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 22 \u0646\u0648\u0627\u0645\u0628\u0631 2023 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0646\u0648\u0627\u0645\u0628\u0631 2023 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n<p style=\"direction:ltr;\">We study 3D point cloud attribute compression via a volumetric approach: assuming point cloud geometry is known at both encoder and decoder, parameters $\u03b8$ of a continuous attribute function $f: \\mathbb{R}^3 \\mapsto \\mathbb{R}$ are quantized to $\\hat\u03b8$ and encoded, so that discrete samples $f_{\\hat\u03b8}(\\mathbf{x}_i)$ can be recovered at known 3D points $\\mathbf{x}_i \\in \\mathbb{R}^3$ at the decoder. Specifically, we consider a nested sequences of function subspaces $\\mathcal{F}^{(p)}_{l_0} \\subseteq \\cdots \\subseteq \\mathcal{F}^{(p)}_L$, where $\\mathcal{F}_l^{(p)}$ is a family of functions spanned by B-spline basis functions of order $p$, $f_l^*$ is the projection of $f$ on $\\mathcal{F}_l^{(p)}$ and encoded as low-pass coefficients $F_l^*$, and $g_l^*$ is the residual function in orthogonal subspace $\\mathcal{G}_l^{(p)}$ (where $\\mathcal{G}_l^{(p)} \\oplus \\mathcal{F}_l^{(p)} = \\mathcal{F}_{l+1}^{(p)}$) and encoded as high-pass coefficients $G_l^*$. In this paper, to improve coding performance over [1], we study predicting $f_{l+1}^*$ at level $l+1$ given $f_l^*$ at level $l$ and encoding of $G_l^*$ for the $p=1$ case (RAHT($1$)). For the prediction, we formalize RAHT(1) linear prediction in MPEG-PCC in a theoretical framework, and propose a new nonlinear predictor using a polynomial of bilateral filter. We derive equations to efficiently compute the critically sampled high-pass coefficients $G_l^*$ amenable to encoding. We optimize parameters in our resulting feed-forward network on a large training set of point clouds by minimizing a rate-distortion Lagrangian. Experimental results show that our improved framework outperformed the MPEG G-PCC predictor by $11$ to $12\\%$ in bit rate reduction.<\/p>\n<h2>\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n<p>\u0645\u0627 \u0641\u0634\u0631\u062f\u0647 \u0633\u0627\u0632\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0627\u0628\u0631 3D \u0646\u0642\u0637\u0647 \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u06cc\u06a9 \u0631\u0648\u06cc\u06a9\u0631\u062f \u062d\u062c\u0645\u06cc \u0645\u0637\u0627\u0644\u0639\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645: \u0628\u0627 \u0641\u0631\u0636 \u0627\u06cc\u0646\u06a9\u0647 \u0647\u0646\u062f\u0633\u0647 \u0627\u0628\u0631 \u0646\u0642\u0637\u0647 \u062f\u0631 \u0647\u0631 \u062f\u0648 \u0631\u0645\u0632\u06af\u0630\u0627\u0631 \u0648 \u0631\u0645\u0632\u06af\u0634\u0627\u06cc\u06cc \u0634\u0646\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a \u060c \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc $ \u03b8 $ \u06cc\u06a9 \u062a\u0627\u0628\u0639 \u0648\u06cc\u0698\u06af\u06cc \u0645\u062f\u0627\u0648\u0645 $ f: \\ mathbb {r}^3 \\ mapsto \\ mathbb {r}$ \u0628\u0647 $ \\ hat\u03b8 $ \u0648 \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a \u060c \u0628\u0647 \u0637\u0648\u0631\u06cc \u06a9\u0647 \u0646\u0645\u0648\u0646\u0647 \u0647\u0627\u06cc \u06af\u0633\u0633\u062a\u0647 $ f _ {\\ hat\u03b8} (\\ mathbf {x} _i) $ \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u062f\u0631 \u0646\u0642\u0627\u0637 3D \u0634\u0646\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 $ \\ mathbf {x} _i \\ \u062f\u0631 \\ mathbb {r \u0628\u0627\u0632\u06cc\u0627\u0628\u06cc \u06a9\u0631\u062f.}^3 $ \u062f\u0631 \u0631\u0645\u0632\u06af\u0634\u0627\u06cc\u06cc.\u0628\u0647 \u0637\u0648\u0631 \u062e\u0627\u0635 \u060c \u0645\u0627 \u062a\u0648\u0627\u0644\u06cc \u062a\u0648 \u062f\u0631 \u062a\u0648 \u062f\u0631 \u062a\u0648 \u062f\u0631 \u062a\u0648 \u062f\u0631 \u0646\u0638\u0631 \u0645\u06cc \u06af\u06cc\u0631\u06cc\u0645 \\ mathcal \\ mathcal {f}^{(p)} _ {l_0} \\ subseteq \\ cdots \\ subseteq \\ mathcal {f}^{(p)} _ l $ \u060c \u062c\u0627\u06cc\u06cc \u06a9\u0647 $ \\ mathcalal{f} _l^{(p)} $ \u062e\u0627\u0646\u0648\u0627\u062f\u0647 \u0627\u06cc \u0627\u0632 \u062a\u0648\u0627\u0628\u0639 \u0627\u0633\u062a \u06a9\u0647 \u062a\u0648\u0633\u0637 \u062a\u0648\u0627\u0628\u0639 \u067e\u0627\u06cc\u0647 b-spline \u0633\u0641\u0627\u0631\u0634 $ p $ \u060c $ f_l^*$ \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc $ f $ \u062f\u0631 $ \\ mathcal {f} _l^\u0627\u0633\u062a.{(p)} $ \u0648 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0636\u0631\u0627\u06cc\u0628 \u06a9\u0645 \u06af\u0630\u0631 $ f_l^*$ \u060c \u0648 $ g_l^*$ \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a.Mathcal {g} _l^{(p)} \\ oplus \\ mathcal {f} _l^{(p)} = \\ mathcal {f} _ {l+1}^{(p)} $) \u0648 \u0628\u0647 \u0635\u0648\u0631\u062a \u0628\u0627\u0644\u0627 \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a\u0636\u0631\u0627\u06cc\u0628 \u0639\u0628\u0648\u0631 $ g_l^*$.\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u060c \u0628\u0631\u0627\u06cc \u0628\u0647\u0628\u0648\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0631\u0646\u0627\u0645\u0647 \u0646\u0648\u06cc\u0633\u06cc \u0628\u06cc\u0634 \u0627\u0632 [1] \u060c \u0645\u0627 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645 $ f_ {l+1}^*$ \u062f\u0631 \u0633\u0637\u062d $ l+1 $ \u0628\u0627 \u062f\u0627\u062f\u0647 $ f_l^*$ \u062f\u0631 \u0633\u0637\u062d $ l $ \u0648 \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u06cc $ g_l^*$ \u0628\u0631\u0627\u06cc \u067e\u0631\u0648\u0646\u062f\u0647 $ p = 1 $ (RAHT (1 $ $)).\u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u060c \u0645\u0627 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062e\u0637\u06cc RAHT (1) \u062f\u0631 MPEG-PCC \u0631\u0627 \u062f\u0631 \u06cc\u06a9 \u0686\u0627\u0631\u0686\u0648\u0628 \u0646\u0638\u0631\u06cc \u0631\u0633\u0645\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u06cc\u06a9 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u06a9\u0646\u0646\u062f\u0647 \u063a\u06cc\u0631\u062e\u0637\u06cc \u062c\u062f\u06cc\u062f \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0686\u0646\u062f \u062c\u0645\u0644\u0647 \u0627\u06cc \u0627\u0632 \u0641\u06cc\u0644\u062a\u0631 \u062f\u0648 \u062c\u0627\u0646\u0628\u0647 \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.\u0645\u0627 \u0645\u0639\u0627\u062f\u0644\u0627\u062a \u0631\u0627 \u0628\u0631\u0627\u06cc \u0645\u062d\u0627\u0633\u0628\u0647 \u06a9\u0627\u0631\u0622\u0645\u062f \u0636\u0631\u0627\u06cc\u0628 \u0628\u0627 \u06af\u0630\u0634\u062a \u0632\u0645\u0627\u0646 \u0627\u0632 \u0646\u0638\u0631 \u0646\u0642\u062f\u06cc \u0627\u0632 $ g_l^*$ \u0642\u0627\u0628\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u06cc \u0628\u0631\u0627\u06cc \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645.\u0645\u0627 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0631\u0627 \u062f\u0631 \u0634\u0628\u06a9\u0647 \u0641\u06cc\u062f \u0641\u06cc\u062f\u0631 \u062d\u0627\u0635\u0644 \u062f\u0631 \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634 \u0628\u0632\u0631\u06af \u0627\u0632 \u0627\u0628\u0631\u0647\u0627\u06cc \u0646\u0642\u0637\u0647 \u0628\u0627 \u0628\u0647 \u062d\u062f\u0627\u0642\u0644 \u0631\u0633\u0627\u0646\u062f\u0646 \u06cc\u06a9 Lagrangian \u0628\u0627 \u0633\u0631\u0639\u062a \u062a\u062e\u0631\u06cc\u0628 \u0628\u0647\u06cc\u0646\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645.\u0646\u062a\u0627\u06cc\u062c \u062a\u062c\u0631\u0628\u06cc \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 \u0686\u0627\u0631\u0686\u0648\u0628 \u0628\u0647\u0628\u0648\u062f \u06cc\u0627\u0641\u062a\u0647 \u0645\u0627 \u0627\u0632 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u06a9\u0646\u0646\u062f\u0647 MPEG G-PCC \u0628\u0627 11 \u062f\u0644\u0627\u0631 \u062a\u0627 12 $ \\ $ $ \u062f\u0631 \u06a9\u0627\u0647\u0634 \u0646\u0631\u062e \u0628\u06cc\u062a \u0639\u0645\u0644 \u06a9\u0631\u062f\u0647 \u0627\u0633\u062a.<\/p>\n<table class=\"table table-bordered table-primary\">\r\n<tr>\r\n\t<td>\r\n\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0632\u0628\u0627\u0646 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc \u0627\u0633\u062a.\r\n\t<\/td>\r\n<\/tr>\r\n\t\r\n<tr>\r\n\t<td>\r\n     \u0628\u0631\u0627\u06cc \u0633\u0641\u0627\u0631\u0634 \u062a\u0631\u062c\u0645\u0647 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0648\u0634 \u0647\u0627\u06cc \u062a\u0645\u0627\u0633\u060c \u067e\u06cc\u0627\u0645\u06a9\u060c \u062a\u0644\u06af\u0631\u0627\u0645 \u0648 \u06cc\u0627 \u0648\u0627\u062a\u0633 \u0627\u067e \u0628\u0627 \u0634\u0645\u0627\u0631\u0647 \u0632\u06cc\u0631 \u062a\u0645\u0627\u0633 \u0628\u06af\u06cc\u0631\u06cc\u062f:\r\n\t<br \/><br \/>\r\n\t09395106248\r\n\t\t\r\n\t\t\r\n\t<br \/><br \/>\r\n\t\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u0634\u0631\u0627\u06cc\u0637 \u062a\u0631\u062c\u0645\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u0627\u0633\u062a:\r\n\t\t<br \/>\r\n\t\t<ul>\r\n\t\t<li>\r\n\t\t\u0642\u06cc\u0645\u062a \u0647\u0631 \u0635\u0641\u062d\u0647 \u062a\u0631\u062c\u0645\u0647 \u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 40 \u0647\u0632\u0627\u0631 \u062a\u0648\u0645\u0627\u0646 \u0645\u06cc \u0628\u0627\u0634\u062f. \r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u062a\u062d\u0648\u06cc\u0644 \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f \u0645\u06cc \u0628\u0627\u0634\u062f.\r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u0635\u0648\u0631\u062a \u062f\u0627\u0634\u062a\u0646 \u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a \u0639\u0627\u062f\u06cc \u0628\u06cc\u0646 3 \u062a\u0627 5 \u0631\u0648\u0632 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t\r\n\t\t<li>\r\n\t\t\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627 \u0645\u06cc \u0628\u0627\u0634\u062f. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.\r\n\t\t<\/li>\r\n\t<\/td>\r\n\t\r\n\t\r\n<\/tr>\r\n<\/table>\r\n\r\n\r\n\n","protected":false},"excerpt":{"rendered":"<p>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc Learned Nonlinear Predictor for Critically Sampled 3D Point Cloud Attribute Compression \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 [&hellip;]<\/p>\n","protected":false},"featured_media":27,"comment_status":"open","ping_status":"closed","template":"","meta":{"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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property=\"og:locale\" content=\"fa_IR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u0645\u0642\u0627\u0644\u0647 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u06a9\u0646\u0646\u062f\u0647 \u063a\u06cc\u0631\u062e\u0637\u06cc \u0622\u0645\u0648\u062e\u062a\u0647\u200c\u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u0641\u0634\u0631\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u0627\u0628\u0631 \u0646\u0642\u0637\u0647 \u0633\u0647\u200c\u0628\u0639\u062f\u06cc \u0646\u0645\u0648\u0646\u0647\u200c\u06af\u06cc\u0631\u06cc \u0627\u0646\u062a\u0642\u0627\u062f\u06cc - \u0641\u0631\u0648\u0634\u06af\u0627\u0647 \u0627\u06a9\u0633\u067e\u0631\u0633\" \/>\n<meta property=\"og:description\" content=\"\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc Learned Nonlinear Predictor for Critically Sampled 3D Point Cloud Attribute Compression \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/express24.ir\/d\/product\/\u0645\u0642\u0627\u0644\u0647-\u067e\u06cc\u0634\u0628\u06cc\u0646\u06cc\u06a9\u0646\u0646\u062f\u0647-\u063a\u06cc\u0631\u062e\u0637\u06cc-\u0622\u0645\u0648\u062e\u062a\u0647\/\" \/>\n<meta property=\"og:site_name\" content=\"\u0641\u0631\u0648\u0634\u06af\u0627\u0647 \u0627\u06a9\u0633\u067e\u0631\u0633\" \/>\n<meta property=\"article:modified_time\" content=\"2024-02-29T05:19:32+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/express24.ir\/d\/wp-content\/uploads\/2024\/02\/Elsevier_logo_2019.svg_.png\" \/>\n\t<meta property=\"og:image:width\" content=\"440\" \/>\n\t<meta property=\"og:image:height\" content=\"486\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" 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