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‎GaussianLSS/index.html‎

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<h1 class="title is-0.1 publication-title">GaussianLSS: Toward Real-world BEV Perception: Depth Uncertainty Estimation
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via Gaussian Splatting</h1>
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<div class="is-size-5 publication-authors">
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<a href="FIRST AUTHOR PERSONAL LINK" target="_blank">Shu-Wei Lu</a><sup>1</sup>,</span>
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<a href="https://sites.google.com/site/yihsuantsai/" target="_blank">Yi-Hsuan Tsai</a><sup>2</sup>,</span>
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<a href="https://sites.google.com/site/yitingchen0524/home" target="_blank">Yi-Ting Chen</a><sup>1*</sup>
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<span class="author-block">National Yang Ming Chiao Tung University<sup>1</sup> Atmanity<sup>2</sup><br>CVPR 2025</span>
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<span class="eql-cntrb"><small><br><sup>*</sup>: Corresponding author</small></span>
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<h2 class="title is-3">Abstract</h2>
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<p>
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In this work, we introduce GaussianLSS, a novel uncertainty-aware BEV perception framework that revisits unprojection-based methods, specifically the Lift-Splat-Shoot (LSS) paradigm, and enhances them with depth uncertainty modeling. GaussianLSS represents spatial dispersion by learning a soft depth mean and computing the variance of the depth distribution, which implicitly captures object extents. We then transform the depth distribution into 3D Gaussians and rasterize them to construct uncertainty-aware BEV features.
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This page was built using the <a href="https://github.com/eliahuhorwitz/Academic-project-page-template" target="_blank">Academic Project Page Template</a> which was adopted from the <a href="https://nerfies.github.io" target="_blank">Nerfies</a> project page.
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You are free to borrow the source code of this website, we just ask that you link back to this page in the footer. <br> This website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/" target="_blank">Creative
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Commons Attribution-ShareAlike 4.0 International License</a>.
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