Scalable wavelet-based coding of irregular meshes with interactive region-of-interest support

This paper proposes a novel functionality in wavelet-based irregular mesh coding, which is interactive region-of-interest (ROI) support. The proposed approach enables the user to define the arbitrary ROIs at the decoder side and to prioritize and decode these regions at arbitrarily high-granularity levels. In this context, a novel adaptive wavelet transform for irregular meshes is proposed, which enables: 1) varying the resolution across the surface at arbitrarily fine-granularity levels and 2) dynamic tiling, which adapts the tile sizes to the local sampling densities at each resolution level. The proposed tiling approach enables a rate-distortion-optimal distribution of rate across spatial regions. When limiting the highest resolution ROI to the visible regions, the fine granularity of the proposed adaptive wavelet transform reduces the required amount of graphics memory by up to 50%. Furthermore, the required graphics memory for an arbitrary small ROI becomes negligible compared to rendering without ROI support, independent of any tiling decisions. Random access is provided by a novel dynamic tiling approach, which proves to be particularly beneficial for large models of over 10(6) similar to 10(7) vertices. The experiments show that the dynamic tiling introduces a limited lossless rate penalty compared to an equivalent codec without ROI support. Additionally, rate savings up to 85% are observed while decoding ROIs of tens of thousands of vertices

Extreme asset simplification and the preservation of visual appearance

Reusing animation film assets for real-time rendering requires extreme simplification. As well-known simplification approaches do not suffice, studios are still forced to manually simplify their assets. To automate this, we employ a pipeline for efficient geometry-based simplification and make use of normal mapping to ensure visual similarity. Our obtained results are promising: geometric complexity is vastly reduced while maintaining a recognizable model, unlike results with classical simplification approaches as employed by commercial applications. We have compared the approaches in two settings, aiming at a similar number of triangles and aiming at a similar storage size, both of which prove that our extreme asset simplification is a valid alternative for classical topological simplification approaches

Path caching in real-time strategy games

This paper proposes a performance optimization for search-based path-planning simulations with the aim to improve systemic scalability. Instead of clustering path requests per agent group, paths are cached at path-request time. Paths are cloned or re-used after selection based on request characteristics, agent properties and external matching criteria. Graph search effort is reduced proportional to the number of agents with similar nearby destinations, while the unique navigational behavior of each individual agent remains unchanged and intact. Formation coherence is maximal for homogeneous navigation, and agent response time improves significantly for large agent groups compared to solutions without this optimization. Explicit multi-agent consensus models are not required and behavioral discontinuities are avoided

Schaalbare karakteristiekbehoudende codering van onregelmatige meshes met ondersteuning voor interactieve interessegebieden /

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