{"id":1778,"date":"2026-05-12T16:22:02","date_gmt":"2026-05-12T16:22:02","guid":{"rendered":"https:\/\/hria.utcluj.ro\/?p=1778"},"modified":"2026-05-18T20:05:36","modified_gmt":"2026-05-18T20:05:36","slug":"perceptia-si-intelegerea-scenelor-3d-din-imagini-aeriene","status":"publish","type":"post","link":"https:\/\/hria.utcluj.ro\/en\/perceptia-si-intelegerea-scenelor-3d-din-imagini-aeriene\/","title":{"rendered":"Percep\u021bia \u0219i \u00een\u021belegerea scenelor 3D din imagini aeriene"},"content":{"rendered":"<p class=\"wp-block-paragraph\" id=\"p-rc_8aa72877c1b59b65-401\">Cercet\u0103rile desf\u0103\u0219urate vizeaz\u0103 crearea unui cadru unitar pentru interpretarea datelor vizuale captate de platforme aeriene (UAV), trec\u00e2nd de la procesarea pixelilor la reprezent\u0103ri semantice \u0219i structurale complexe.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Arhitectura de percep\u021bie pe trei niveluri<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_8aa72877c1b59b65-402\">Sistemul este organizat ierarhic pentru a transforma datele brute \u00een decizii fundamentate<sup><\/sup>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Nivelul 1 &#8211; Percep\u021bia Vizual\u0103 (Visual Perception):<\/strong> Se concentreaz\u0103 pe extragerea tr\u0103s\u0103turilor fundamentale din imagini RGB sau fluxuri video. Include predictori pentru ad\u00e2ncime dens\u0103 (<em>Dense Depth<\/em>), metode de segmentare panoptic\u0103 (generarea m\u0103\u0219tilor semantice \u0219i de instan\u021b\u0103) \u0219i algoritmi de detec\u021bie a obiectelor \u00een spa\u021biul 3D.<\/li>\n\n\n\n<li><strong>Nivelul 2 &#8211; Reconstruc\u021bia Neural\u0103 a Scenei (Neural Scene Reconstruction):<\/strong> Utilizeaz\u0103 predictori neurali statici \u0219i dinamici pentru a genera modele 3D. Aceast\u0103 etap\u0103 permite randarea de imagini proiective, h\u0103r\u021bi de ad\u00e2ncime, vederi ortografice (<em>Ortho<\/em>) \u0219i proiec\u021bii de tip <em>Bird&#8217;s Eye View<\/em> (BEV). De asemenea, se realizeaz\u0103 localizarea pe h\u0103r\u021bi de tip <em>Open Street Map<\/em> (OSM).<\/li>\n\n\n\n<li><strong>Nivelul 3 &#8211; Graful de Scene (Scene Graph):<\/strong> Reprezint\u0103 nivelul superior de abstractizare unde se realizeaz\u0103 detec\u021bia schimb\u0103rilor, analiza propriet\u0103\u021bilor drumurilor \u0219i detectarea modului de utilizare a terenului (<em>Land use Detection<\/em>). Acesta serve\u0219te drept interfa\u021b\u0103 pentru agentul de luare a deciziilor.<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"443\" src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/perception-architecture-1024x443.png\" alt=\"\" class=\"wp-image-1779\" srcset=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/perception-architecture-1024x443.png 1024w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/perception-architecture-300x130.png 300w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/perception-architecture-768x333.png 768w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/perception-architecture-18x8.png 18w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/perception-architecture.png 1337w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div>\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Dataset-ul sintetic ClaraVid<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_8aa72877c1b59b65-406\">Pentru a antrena modele capabile s\u0103 gestioneze scenarii complexe, a fost dezvoltat setul de date <strong>ClaraVid<\/strong>, care ofer\u0103 avantaje majore fa\u021b\u0103 de seturile tradi\u021bionale<sup><\/sup>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Achizi\u021bie Multi-View:<\/strong> Permite captarea simultan\u0103 din multiple puncte de vedere (High, Mid, Low angle) pentru scenarii de \u00eenalt\u0103 rezolu\u021bie.<\/li>\n\n\n\n<li><strong>Etichetare Multimodal\u0103 Complet\u0103:<\/strong> Include nori de puncte (PCL) la nivel de scen\u0103, m\u0103\u0219ti pentru obiecte dinamice, segmente panoptice (semantice \u0219i de instan\u021b\u0103) \u0219i h\u0103r\u021bi de ad\u00e2ncime precise.<\/li>\n\n\n\n<li><strong>Accesibilitate:<\/strong> Datele sunt disponibile public pe platforma Hugging Face: <a href=\"http:\/\/huggingface.co\/datasets\/radubeche\/claravid\" data-type=\"link\" data-id=\"huggingface.co\/datasets\/radubeche\/claravid\">huggingface.co\/datasets\/radubeche\/claravid<\/a>.<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" width=\"1024\" height=\"495\" src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/claravid1-1024x495.png\" alt=\"\" class=\"wp-image-1780\" srcset=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/claravid1-1024x495.png 1024w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/claravid1-300x145.png 300w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/claravid1-768x371.png 768w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/claravid1-18x9.png 18w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/claravid1.png 1270w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" width=\"1024\" height=\"351\" src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/claravid2-1024x351.png\" alt=\"\" class=\"wp-image-1781\" srcset=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/claravid2-1024x351.png 1024w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/claravid2-300x103.png 300w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/claravid2-768x263.png 768w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/claravid2-18x6.png 18w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/claravid2.png 1470w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div>\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">UAVid++ \u2013 Segmentare semantic\u0103 avansat\u0103 pentru imagini UAV<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Percep\u021bia vizual\u0103 \u00een scenarii aeriene (UAV) ridic\u0103 provoc\u0103ri semnificative comparativ cu imaginile la nivelul solului. Printre principalele dificult\u0103\u021bi se num\u0103r\u0103:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>varia\u021bii extreme de scar\u0103 (de la cl\u0103diri mari la obiecte foarte mici precum pietoni sau vehicule \u00eendep\u0103rtate)<\/li>\n\n\n\n<li>densitate ridicat\u0103 \u00een zone urbane<\/li>\n\n\n\n<li>ambiguit\u0103\u021bi vizuale cauzate de unghiuri de observa\u021bie \u0219i altitudine<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Dataset-urile existente, inclusiv UAVid, prezint\u0103 limit\u0103ri importante:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>adnot\u0103ri coarse (aproximative)<\/li>\n\n\n\n<li>inconsisten\u021be semantice \u00eentre clase<\/li>\n\n\n\n<li>lipsa detaliilor fine la nivel de contur<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Aceste limit\u0103ri afecteaz\u0103 direct performan\u021ba modelelor de segmentare, \u00een special \u00een ceea ce prive\u0219te obiectele mici \u0219i generalizarea \u00een scenarii noi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Pentru a dep\u0103\u0219i aceste limit\u0103ri, am dezvoltat <strong>UAVid++<\/strong>, o versiune \u00eembun\u0103t\u0103\u021bit\u0103 semnificativ a dataset-ului UAVid, care introduce:<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Adnot\u0103ri de \u00eenalt\u0103 fidelitate<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>corectarea manual\u0103 \u0219i automat\u0103 a etichetelor existente<\/li>\n\n\n\n<li>aliniere semantic\u0103 riguroas\u0103 \u00eentre cadre<\/li>\n\n\n\n<li>contururi mult mai precise la nivel de pixe<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Taxonomie semantic\u0103 extins\u0103<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>introducerea de clase suplimentare relevante pentru scenarii aeriene (ex: ap\u0103, cer, acoperi\u0219uri)<\/li>\n\n\n\n<li>structur\u0103 mai coerent\u0103 \u0219i compatibil\u0103 cu alte dataset-uri UAV<\/li>\n\n\n\n<li>suport pentru evalu\u0103ri out-of-distribution<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Segmentare fin\u0103 \u00een scenarii complexe<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u00eembun\u0103t\u0103\u021birea reprezent\u0103rii obiectelor mici \u0219i aglomerate<\/li>\n\n\n\n<li>separare mai clar\u0103 \u00eentre instan\u021be apropiate<\/li>\n\n\n\n<li>reducerea ambiguit\u0103\u021bilor \u00een zone urbane dense<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"739\" src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/uavid1-1024x739.png\" alt=\"\" class=\"wp-image-1782\" srcset=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/uavid1-1024x739.png 1024w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/uavid1-300x216.png 300w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/uavid1-768x554.png 768w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/uavid1-18x12.png 18w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/uavid1.png 1163w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div>\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">Integrarea modelelor de tip Large Vision Models (LVM)<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Pentru a valorifica pe deplin noul dataset, s-a propus o arhitectur\u0103 hibrid\u0103 care combin\u0103:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>backbone DINO (self-supervised Vision Transformer)<\/strong><br>\u2192 ofer\u0103 reprezent\u0103ri semantice robuste \u0219i capacitate excelent\u0103 de generalizare<\/li>\n\n\n\n<li><strong>head inspirat din U-Net<\/strong><br>\u2192 recupereaz\u0103 detalii spa\u021biale fine \u0219i \u00eembun\u0103t\u0103\u021be\u0219te precizia contururilor<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Aceast\u0103 combina\u021bie abordeaz\u0103 un compromis esen\u021bial \u00een percep\u021bia vizual\u0103:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>modelele mari (LVM) \u2192 \u00een\u021beleg bine contextul global, dar pierd detalii fine<\/li>\n\n\n\n<li>modelele specializate \u2192 ofer\u0103 precizie local\u0103, dar generalizeaz\u0103 mai slab<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Prin integrarea lor, ob\u021binem un model capabil s\u0103 gestioneze simultan <strong>contextul global \u0219i detaliul local<\/strong>.<\/p>\n\n\n\n<figure class=\"wp-block-video\"><video height=\"854\" style=\"aspect-ratio: 2424 \/ 854;\" width=\"2424\" controls src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/small_object_segmentation.mp4\"><\/video><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Experimentele realizate demonstreaz\u0103 \u00eembun\u0103t\u0103\u021biri consistente:<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Generalizare superioar\u0103<\/strong><br>Modelele antrenate pe UAVid++ performeaz\u0103 robust pe scene, altitudini \u0219i orient\u0103ri diferite ale camerei.<\/li>\n\n\n\n<li><strong>Segmentare \u00eembun\u0103t\u0103\u021bit\u0103 a obiectelor mici<\/strong><br>Detectarea \u0219i delimitarea obiectelor de dimensiuni reduse este semnificativ mai precis\u0103.<\/li>\n\n\n\n<li><strong>Contururi mai clare \u0219i mai stabile<\/strong><br>Marginile obiectelor sunt mai bine definite, reduc\u00e2nd erorile de clasificare la grani\u021be.<\/li>\n\n\n\n<li><strong>Calitate apropiat\u0103 de ground truth<\/strong><br>Nivelul ridicat al adnot\u0103rilor permite utilizarea dataset-ului pentru:\n<ul class=\"wp-block-list\">\n<li>knowledge distillation<\/li>\n\n\n\n<li>evalu\u0103ri riguroase ale modelelor<\/li>\n\n\n\n<li>benchmark-uri realiste<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-video\"><video height=\"714\" style=\"aspect-ratio: 1632 \/ 714;\" width=\"1632\" controls src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/small_object_segmentation_results_convert.mp4\"><\/video><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Estimarea ad\u00e2ncimii monoculare \u0219i scalarea geo-informat\u0103<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"p-rc_8aa72877c1b59b65-410\">O provocare major\u0103 \u00een percep\u021bia aerian\u0103 este ob\u021binerea unei ad\u00e2ncimi metrice corecte din imagini monoculare. Solu\u021bia propus\u0103 include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>UAVid-3D-Scenes:<\/strong> O extensie a setului semantic UAVid, axat\u0103 pe ad\u00e2ncime, care permite cercetarea comun\u0103 a sarcinilor semantice \u0219i 3D.<\/li>\n\n\n\n<li><strong>Integrarea TanDEM-X GDEM:<\/strong> Se utilizeaz\u0103 date globale de eleva\u021bie (Digital Elevation Model) pentru a asigura o scalare metric\u0103 corect\u0103 a modelelor de ad\u00e2ncime relativ\u0103.<\/li>\n\n\n\n<li><strong>Fluxul de Procesare a Datelor Real-World:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Alinierea coordonatelor GPS cu imagini satelitare pentru setul UAVid.<\/li>\n\n\n\n<li>Utilizarea algoritmului <strong>Cloth Simulation Filter (CSF)<\/strong> pentru segmentarea eficient\u0103 a solului.<\/li>\n\n\n\n<li>Proiec\u021bia \u0219i mascarea datelor pentru corelarea precis\u0103 \u00eentre cadrele camerei \u0219i modelul digital de teren.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"220\" src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/tandem-x-1024x220.png\" alt=\"\" class=\"wp-image-1785\" style=\"width:1170px;height:auto\" srcset=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/tandem-x-1024x220.png 1024w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/tandem-x-300x64.png 300w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/tandem-x-768x165.png 768w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/tandem-x-1536x330.png 1536w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/tandem-x-18x4.png 18w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/tandem-x.png 1780w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div>\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">UAVid-3D-Scenes \u2013 Extensie orientat\u0103 pe ad\u00e2ncime<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>UAVid-3D-Scenes<\/strong> reprezint\u0103 o extensie a dataset-ului UAVid, orientat\u0103 c\u0103tre integrarea informa\u021biei de ad\u00e2ncime, pentru a permite aplica\u021bii care combin\u0103 date semantice \u0219i 3D. Dataset-ul este disponibil public pe HuggingFace: <a href=\"https:\/\/huggingface.co\/datasets\/hrflr\/uavid-3d-scenes\">huggingface.co\/datasets\/hrflr\/uavid-3d-scenes<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Dataset-ul include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>ad\u00e2ncime dens\u0103 \u0219i rar\u0103<\/li>\n\n\n\n<li>alinierea cadrelor UAVid la coordonate globale (folosind GPS \u0219i imagini satelitare)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Pentru fiecare cadru, sunt integrate date din modelul de eleva\u021bie <strong>TanDEM-X (GDEM)<\/strong>, utilizate pentru proiec\u021bia informa\u021biei de teren.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">De asemenea, sunt utilizate reconstruc\u021bii 3D din <strong>UFO Depth dataset<\/strong> pentru evalu\u0103ri out-of-distribution.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Scopul este \u00eembun\u0103t\u0103\u021birea scal\u0103rii metrice a estim\u0103rilor de ad\u00e2ncime, prin combinarea informa\u021biilor de pozi\u021bie, altitudine \u0219i modele digitale de teren.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"783\" height=\"280\" src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/ufod.png\" alt=\"\" class=\"wp-image-1786\" srcset=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/ufod.png 783w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/ufod-300x107.png 300w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/ufod-768x275.png 768w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/ufod-18x6.png 18w\" sizes=\"(max-width: 783px) 100vw, 783px\" \/><\/figure>\n<\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"903\" height=\"284\" src=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/uavid-tandemx.png\" alt=\"\" class=\"wp-image-1787\" srcset=\"https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/uavid-tandemx.png 903w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/uavid-tandemx-300x94.png 300w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/uavid-tandemx-768x242.png 768w, https:\/\/hria.utcluj.ro\/wp-content\/uploads\/2026\/05\/uavid-tandemx-18x6.png 18w\" sizes=\"(max-width: 903px) 100vw, 903px\" \/><\/figure>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Cercet\u0103rile desf\u0103\u0219urate vizeaz\u0103 crearea unui cadru unitar pentru interpretarea datelor vizuale captate de platforme aeriene (UAV), trec\u00e2nd de la procesarea pixelilor la reprezent\u0103ri semantice \u0219i structurale complexe. Arhitectura de percep\u021bie pe trei niveluri Sistemul este organizat ierarhic pentru a transforma datele brute \u00een decizii fundamentate: Dataset-ul sintetic ClaraVid Pentru a antrena modele capabile s\u0103 gestioneze&hellip;&nbsp;<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"default","neve_meta_enable_content_width":"on","neve_meta_content_width":100,"neve_meta_title_alignment":"","neve_meta_author_avatar":"off","neve_post_elements_order":"[\"content\"]","neve_meta_disable_header":"off","neve_meta_disable_footer":"","neve_meta_disable_title":"","_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1778","post","type-post","status-publish","format-standard","hentry","category-rezultate"],"_links":{"self":[{"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/posts\/1778","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/comments?post=1778"}],"version-history":[{"count":3,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/posts\/1778\/revisions"}],"predecessor-version":[{"id":1845,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/posts\/1778\/revisions\/1845"}],"wp:attachment":[{"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/media?parent=1778"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/categories?post=1778"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hria.utcluj.ro\/en\/wp-json\/wp\/v2\/tags?post=1778"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}