Visual Data Representation using Context-Aware Samples

The rapid growth in the complexity of geometry models has necessisated revision of several conventional techniques in computer graphics. At the heart of this trend is the representation of geometry with locally constant approximations using independent sample primitives. This generally leads to a higher sampling rate and thus a high cost of representation, transmission, and rendering. We advocate an alternate approach involving context-aware samples that capture the local variation of the geometry. We detail two approaches; one, based on differential geometry and the other based on statistics. Our differential-geometry-based approach captures the context of the local geometry using an estimation of the local Taylor's series expansion. We render such samples using programmable Graphics Processing Unit (GPU) by fast approximation of the geometry in the screen space. The benefits of this representation can also be seen in other applications such as simulation of light transport. In our statistics-based approach we capture the context of the local geometry using Principal Component Analysis (PCA). This allows us to achieve hierarchical detail by modeling the geometry in a non-deterministic fashion as a hierarchical probability distribution. We approximate the geometry and its attributes using quasi-random sampling. Our results show a significant rendering speedup and savings in the geometric bandwidth when compared to current approaches

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Traditional geometry representations have focused on representing the details of the geometry in a deterministic fashion. In this paper we propose a statistical representation of the geometry that leverages local coherence for very large datasets. We show how the statistical analysis of a densely sampled point model can be used to improve the geometry bandwidth bottleneck both on the system bus and over the network and for randomized rendering without sacrificing visual realism. Our statistical representation is built using a clustering-based hierarchical principal component analysis (PCA) of the point geometry. It gives us a hierarchical partitioning of the geometry into compact local nodes representing attributes such as spatial coordinates, normal, and color. We pack this information into a few bytes using classification and quantization. This allows our representation to directly render from compressed format for efficient remote as well as local rendering. Our representation supports view-dependent as well as on-demand rendering. Our approach renders each node using quasi-random sampling using the probability distribution derived from the PCA analysis. We show many benefits of our approach: (1) several-fold improvement in the storage and transmission complexity of point geometry, (2) direct rendering from compressed data, and (3) support for local and remote rendering on a variety of rendering platforms such as CPUs, GPUs, and PDAs

Differential point rendering

Abstract. We present a novel point rendering primitive, called Differential Point (DP), that captures the local differential geometry in the vicinity of a sampled point. This is a more general point representation that, for the cost of a few additional bytes, packs much more information per point than the traditional point-based models. This information is used to efficiently render the surface as a collection of local neighborhoods. The advantages to this representation are manyfold: (1) it delivers a significant reduction in the number of point primitives that represent a surface (2) it achieves robust hardware accelerated per-pixel shading – even with no connectivity information (3) it offers a novel point-based simplification technique that has a convenient and intuitive interface for the user to efficiently resolve the speed versus quality tradeoff. The number of primitives being equal, DPs produce a much better quality of rendering than a pure splatbased approach. Visual appearances being similar, DPs are about two times faster and require about � � less disk space in comparison to splatting primitives

Statistical Point Geometry

We propose a scheme for modeling point sample geometry with statistical analysis. In our scheme we depart from the current schemes that deterministically represent the attributes of each point sample. We show how the statistical analysis of a densely sampled point model can be used to improve the geometry bandwidth bottleneck and to do randomized rendering without sacrificing visual realism. We first carry out a hierarchical principal component analysis (PCA) of the model. This stage partitions the model into compact local geometries by exploiting local coherence. Our scheme handles vertex coordinates, normals, and color. The input model is reconstructed and rendered using a probability distribution derived from the PCA analysis. We demonstrate the benefits of this approach in all stages of the graphics pipeline: (1) orders of magnitude improvement in the storage and transmission complexity of point geometry, (2) direct rendering from compressed data, and (3) view-dependent randomized rendering