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‎blog/mesh-aware-pruning.md.html‎

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<meta charset="utf-8" emacsmode="-*- markdown -*-">
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<title>Adaptive Mesh-Guided Pruning</title>
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<link rel="stylesheet" href="https://graphics.cs.utah.edu/research/projects/area-restir/interactive-results-viewer/utils/report.css">
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<link rel="stylesheet" href="script/report.css">
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<script src="script/data.js"></script>
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<script src="https://graphics.cs.utah.edu/research/projects/area-restir/interactive-results-viewer/utils/ImageBox.js"></script>
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<script src="https://graphics.cs.utah.edu/research/projects/area-restir/interactive-results-viewer/utils/CopyrightBox.js"></script>
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# Comparison to MILo's Dense Pruning
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MILo's original pruning relies solely on photometric importance, which can preserve redundant floaters and lead to irregular surfaces. In contrast, AMGP integrates geometric proximity and opacity, yielding more compact and surface-aligned Gaussian sets. This improves reconstruction accuracy and efficiency, reducing Gaussian counts by 30–50% while maintaining or improving PSNR and lowering Chamfer distances.
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# Empirical Evaluation
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MILo's original pruning relies solely on photometric importance, which can preserve redundant floaters and lead to irregular surfaces.
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In contrast, AMGP integrates geometric proximity and opacity, yielding more compact and surface-aligned Gaussian sets.
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This improves reconstruction accuracy and efficiency, reducing Gaussian counts by around 50% while maintaining PSNR.
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<div class="content" id="content"></div>
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‎blog/script/report.css‎

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