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題名 | 漸進式三維模型分享技術=Progressive Sharing of 3D Model |
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作者姓名(中文) | 許碩方; 王任瓚; | 書刊名 | 臺東大學綠色科學學刊 |
卷期 | 1:1 2011.05[民100.05] |
頁次 | 頁143-161 |
分類號 | 312.2 |
關鍵詞 | 三維模型; 三維模型分享; IEEE-754標準浮點數; 3D model; 3D model sharing; IEEE-754 standard floating point; |
語文 | 中文(Chinese) |
中文摘要 | 本研究提出一個漸進式的三維模型分享方法,用以保護重要的三維模型資料。在本研究所提出的n(n ≥ 2)階層漸進式三維模型分享方法中,會將所輸入的三維模型編碼成n 份分存模型,這些分存模型可以分開儲存與傳送。在還原階段,若使用者取得單份分存模型,將無法解出原三維模型資料;而如果使用者擁有q(2 ≤ q ≤ n + 1) 份分存模型時,則可還原出一個版本的三維模型,且所還原出的三維模型之品質與分存數量q 成正比;當使用者拿到n + 1 份的分存模型時,可將原三維模型資料完全無失真重建出。本研究所提的三維模型分享技術,係針對由浮點數構成的三維點資料做設計,開發基於IEEE-754 標準浮點數表示法的分享方法,在分享技術的研究上是一項創新的設計。所設計的n 階層漸進分享方法將原三維模型編碼成n + 1 份較小的分存模型,使各個分存模型的傳輸與儲存更有效率外,在某些分存模型遺失或被破壞的情況下,仍可以重建回一個版本的原三維模型,具高度的容錯性。而隨參與解碼分存模型數量以漸進式還原三維模型資料的設計,提供使用者設計更多不同解密效果的三維模型分享方法。 |
英文摘要 | This paper presents a progressive sharing method for protecting secret 3D (three dimensional) Models. The proposed n-level (n ≥ 2) progressive 3D model sharing method divides the points of a 3D model in n groups, and encodes them in n + 1 shadow models. Each shadow model has noisy appearance, and knowledge of a single shadow model gets nothing about the secret 3D model. A mimicked 3D model can be revealed when q(2 ≤ q ≤ n + 1) shadow models are available. The quality of the revealed model is proportional to the number of shadow models engaged in the decoding process, and the original 3D secret model can be revealed without any loss when all of the n + 1 shadow models are obtained. The proposed sharing method manipulates 3D points of a 3D model represented in real numbers. The sharing function processes directly on numbers represented in IEEE-754 standard format floating point representation, which is novel in the field of sharing technology. In the proposed n-level progressive 3D model sharing method, a secret 3D model is encoded in n + 1 shadow models. Each shadow model occupies smaller storage and can be stored in separated storage. The small-size of each shadow model benefits the further processing such as transmission or storage. Besides, a mimicked model can be reconstructed even when some of the shadow models were crashed or lost, which increases the robustness to the secret model. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。