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<h1 class="title is-1 publication-title">Compact3D: Compressing Gaussian Splat Radiance Field Models with Vector Quantization</h1> | ||
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<a href="https://www.linkedin.com/in/navaneetkl/?originalSubdomain=in">KL Navaneet</a><sup>*</sup>,</span> | ||
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<a href="https://arghavan-kpm.github.io/">Kossar Pourahmadi</a><sup>*</sup>,</span> | ||
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<a href="https://soroush-abbasi.github.io/">Soroush Abbasi Koohpayegani</a>,</span> | ||
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<a href="https://web.cs.ucdavis.edu/~hpirsiav/">Hamed Pirsiavash</a></span> | ||
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<p> | ||
3D Gaussian Splatting is a new method for modeling and rendering 3D radiance fields that achieves much faster | ||
learning and rendering time compared to SOTA NeRF methods. However, it comes with a drawback in the much larger | ||
storage demand compared to NeRF methods since it needs to store the parameters for several 3D Gaussians. | ||
</p> | ||
<p> | ||
We notice that many Gaussians may share similar parameters, so we introduce a simple vector quantization method | ||
based on kmeans algorithm to quantize the Gaussian parameters. Then, we store the small codebook along with the | ||
index of the code for each Gaussian. Moreover, we compress the indices further by sorting them and using a | ||
method similar to run-length encoding. | ||
</p> | ||
<p> | ||
We do extensive experiments on standard benchmarks as well as a new benchmark which is an order of magnitude larger | ||
than the standard benchmarks. We show that our simple yet effective method can reduce the storage cost for the | ||
original 3D Gaussian Splatting method by a factor of almost 20× with a very small drop in the quality of rendered images. | ||
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<h2 class="title">BibTeX</h2> | ||
<pre><code>@inproceedings{navaneet2023compact3d, | ||
author = {Navaneet, K L and Pourahmadi Meibodi, Kossar and Koohpayegani, Soroush Abbasi and Pirsiavash, Hamed}, | ||
title = {Compact3D: Compressing Gaussian Splat Radiance Field Models with Vector Quantization}, | ||
year = {2023} | ||
}</code></pre> | ||
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