Browsing by Subject "3-D modeling"

Now showing 1 - 3 of 3

Open Access
Baǧlanırlıkla yönlendirilmiş uyarlamalı dalgacık dönüşümü ile üç boyutlu model sıkıştırılması
(IEEE, 2007-06) Köse, Kıvanç; Çetin, A. Enis; Güdükbay, Uğur; Onural, Levent
Dikdörtgensel olmayan dalgacık dönüşümüne dayalı çok çözünürlüklü üç boyutlu model sıkıştırılması için iki yöntem önerilmektedir. Bunlar Sıradüzensel Ağaç Yapılarının Kümelere Bölütlenmesi (Set Partitioning In Hierarchical Trees - SPIHT) ve JPEG2000 tekniklerine dayanmaktadır. Üç boyutlu modeller düzenli ızgara yapılar üzerinde tanımlı iki boyutlu imgelere dönüştürülmekte, ve bu gösterim bağlanırlıkla yönlendirilmiş uyarlamalı dalgacık dönüşümünden geçirilerek ortaya çıkan dalgacık kümesi verisi SPITH veya JPEG2000 yöntemlerinden biri uygulanarak bit dizgisine dönüştürülmektedir. SPIHT ile elde edilen bit dizgisinin değişik uzunluklardaki bölümlerinden modelin değişik çözünürlüklerde geri çatmak mümkün olduğundan önerilen bu yöntem modellerin aşamalı gösterimine olanak sağlamaktadır. Dalgacık dönüşümü verilerinin SPIHT ile kodlanmasıyla elde edilen sonuç JPEG2000 ve MPEG-3DGC ile yapılan kodlamanın sonucundan daha başarılı olmuştur. Two compression frameworks that are based on a Set Partitioning In Hierarchical Trees (SPIHT) and JPEG2000 methods are proposed. The 3D mesh is first transformed to 2D images on a regular grid structure. Then, this image-like representation is wavelet transformed employing an adaptive predictor that takes advantage of the connectivity information of mesh vertices. Then SPIHT or JPEG2000 is applied on the wavelet domain data. The SPIHT based method is progressive because the resolution of the reconstructed mesh can be changed by varying the length of the one-dimensional data stream created by SPIHT algorithm. The results of the SPIHT based algorith is observed to be superior to JPEG200 based mesh coder and MPEG-3DGC in rate-distortion.
Open Access
Novel compression algorithm based on sparse sampling of 3-D laser range scans
(Oxford University Press, 2013) Dobrucali, O.; Barshan, B.
Three-dimensional models of environments can be very useful and are commonly employed in areas such as robotics, art and architecture, facility management, water management, environmental/industrial/urban planning and documentation. A 3-D model is typically composed of a large number of measurements. When 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to use the capacity of the communication channel or the storage medium effectively. We propose a novel compression technique based on compressive sampling applied to sparse representations of 3-D laser range measurements. The main issue here is finding highly sparse representations of the range measurements, since they do not have such representations in common domains, such as the frequency domain. To solve this problem, we develop a new algorithm to generate sparse innovations between consecutive range measurements acquired while the sensor moves. We compare the sparsity of our innovations with others generated by estimation and filtering. Furthermore, we compare the compression performance of our lossy compression method with widely used lossless and lossy compression techniques. The proposed method offers a small compression ratio and provides a reasonable compromise between the reconstruction error and processing time. © 2012 The Author 2012. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.
Open Access
A novel compression algorithm based on sparse sampling of 3-D laser range scans
(2010) Dobrucalı, Oğuzcan
3-D models of environments can be very useful and are commonly employed in areas such as robotics, art and architecture, environmental planning and documentation. A 3-D model is typically comprised of a large number of measurements. When 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to use the capacity of the communication channel or the storage medium effectively. In this thesis, we propose a novel compression technique based on compressive sampling, applied to sparse representations of 3-D laser range measurements. The main issue here is finding highly sparse representations of the range measurements, since they do not have such representations in common domains, such as the frequency domain. To solve this problem, we develop a new algorithm to generate sparse innovations between consecutive range measurements acquired while the sensor moves. We compare the sparsity of our innovations with others generated by estimation and filtering. Furthermore, we compare the compression performance of our lossy compression method with widely used lossless and lossy compression techniques. The proposed method offers small compression ratio and provides a reasonable compromise between reconstruction error and processing time.