{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T05:46:22Z","timestamp":1773380782643,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>In response to the core demands of virtual reality (VR) advertising scenarios for high visual fidelity, multi-view coverage, and real-time interaction, traditional 3D reconstruction methods have complex modeling processes (requiring professionals to complete it in several weeks) and insufficient support for dynamic elements (stiff transitions in keyframe animations). Moreover, the original model of neural radiation Field (NeRF) has key bottlenecks such as low generation efficiency (requiring hundreds of images and tens of hours of training) and high rendering overhead (single-frame rendering exceeds 200 ms). This paper proposes a full-chain solution of sparse input rapid reconstruction - lightweight dynamic modeling - hardware adaptation rendering optimization. Firstly, combined with the production characteristics of short cycle and low cost of VR advertising, a sparse NeRF reconstruction framework integrating depth prior and semantic guidance is proposed. Through hierarchical initialization and joint optimization strategies, high-fidelity scene reconstruction is achieved with 15-20 image inputs. Secondly, in response to the dynamic product display requirements in advertisements, a dynamic modeling method based on local deformation fields is designed. Through spatial masks and temporal consistency constraints, the number of parameters is reduced by 95% compared with DyNeRF while ensuring dynamic smoothness (SSIM\u22650.92). Finally, in view of the hardware characteristics of VR (limited computing power on mobile terminals and low latency requirements), a rendering optimization chain of explicit baking - LOD adaptation - GPU parallelization is proposed to achieve stable rendering at 90fps on the Standalone VR platform (Pico 4). The experiment was verified based on the PC\/Standalone dual platforms, the self-built VR-AD-12 dataset (12 advertising scenes), and public datasets (Tanks and Temples, DTU). The results show that the scene generation time of the method proposed in this paper is shortened by 62.3% compared with the original NeRF (24\u00b11.2 h vs. 3.6\u00b10.2 h), the PSNR of dynamic modeling reaches 32.1\u00b10.3 dB, the rendering delay is less than 18 ms (17.8\u00b10.5 ms on Pico 4), the LPIPS is 0.09\u00b10.01, and the FID is 22.3\u00b11.2. Core indicators are superior to existing methods, and the method is adapted to the industrial production requirements of VR advertising.<\/jats:p>","DOI":"10.31449\/inf.v50i9.12804","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:04Z","timestamp":1773354064000},"source":"Crossref","is-referenced-by-count":0,"title":["Efficient Sparse Input Scene Reconstruction and Real-Time Rendering for VR Advertising Using Optimized NeRF Framework"],"prefix":"10.31449","volume":"50","author":[{"given":"Yujuan","family":"Tao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,12]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12804\/6562","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12804\/6562","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:05Z","timestamp":1773354065000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12804"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":0,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,3,12]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i9.12804","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,12]]}}}