Due to the development of depth sensors, such as time-of-flight (ToF) cameras, it becomes easier to acquire
depth information directly from a scene. Although such devices enable us to obtain depth maps at video
frame rates, the depth maps often have low resolutions only. A typical ToF camera retrieves depth maps of
resolution 320 x 200, which is much lower than the resolutions of high definition color images. In this work, we
propose a depth image super-resolution algorithm, which operates robustly even when there is a large resolution
gap between a depth image and a reference color image. To prevent edge smoothing artifacts, which are the
main drawback of conventional techniques, we adopt a superpixel-based approach and develop an edge enhancing
scheme. Simulation results demonstrate that the proposed algorithm aligns the edges of a depth map to accurately
coincide with those of a high resolution color image.
A geometry compression algorithm for 3-D QSplat data using vector quantization (VQ) is proposed in this work. The positions of child spheres are transformed to the local coordinate system, which is determined by the parent children relationship. The coordinate transform makes child positions more compactly distributed in 3-D space, facilitating effective quantization. Moreover, we develop a constrained encoding method for sphere radii, which guarantees hole-free surface rendering at the decoder side. Simulation results show that the proposed algorithm provides a faithful rendering quality even at low bitrates.
KEYWORDS: Optical spheres, Data modeling, 3D modeling, Visualization, 3D image processing, Computer simulations, Algorithm development, Reconstruction algorithms, Data compression, Head
A lossless compression algorithm of 3D point data is proposed in this work. QSplat is one of the efficient rendering methods for 3D point data. In QSplat, each point is assigned a sphere, and the geometry and normal data are stored in the hierarchical structure of bounding spheres. To compress QSplat data, child spheres are sorted based on their limit radii to constrain the indices for the geometry data. Then, the radii and the positions of spheres are encoded separately using the reduced index sets. Also, each normal is encoded using the parent normal context, and the normal indices are reduced by the normal cone information. Simulation results show that the proposed algorithm achieves a high compression ratio by combining the reduced index sets with the context-based entropy coding.
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