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<div class="container is-max-desktop">
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<div class="columns is-centered">
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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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<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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<span class="author-block">
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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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<span class="author-block">
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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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<a href="https://sites.google.com/site/yitingchen0524/home" target="_blank">Yi-Ting Chen</a><sup>1</sup>
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</span>
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<span class="author-block">National Yang Ming Chiao Tung University<sup>1</sup> Atmanity Inc.<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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<a href="https://arxiv.org/pdf/<ARXIV PAPER ID>.pdf" target="_blank"
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<a href="https://arxiv.org/abs/2504.01957" target="_blank"
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<span>arXiv</span>
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<figure style="display: flex; flex-direction: column; align-items: center;">
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<img src="./static/images/prediction.gif" alt="splatting"
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</figure>
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learning to bypass explicit depth estimation. While we observe promising advancements in this paradigm, they still
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fall short of real-world applications because of the lack of
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uncertainty modeling and expensive computational requirement. In this work, we introduce GaussianLSS, a novel
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uncertainty-aware BEV perception framework that revisits unprojection-based methods, specifically the Lift-Splat-Shoot (LSS) paradigm, and enhances them with depth un-certainty modeling. GaussianLSS represents spatial dispersion by learning a soft depth mean and computing the variance of the depth distribution, which implicitly cap-
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tures object extents. We then transform the depth distribution into 3D Gaussians and rasterize them to construct
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uncertainty-aware BEV perception framework that revisits unprojection-based methods, specifically the Lift-Splat-Shoot (LSS) paradigm, and enhances them with depth un-certainty modeling. GaussianLSS represents spatial dispersion by learning a soft depth mean and computing the variance of the depth distribution,
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which implicitly captures object extents. We then transform the depth distribution into 3D Gaussians and rasterize them to construct
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uncertainty-aware BEV features. We evaluate GaussianLSS
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on the nuScenes dataset, achieving state-of-the-art performance compared to unprojection-based methods. In particular, it provides significant advantages in speed, running
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2x faster, and in memory efficiency, using 0.3x less memory compared to projection-based methods, while achieving competitive performance with only a 0.7% IoU difference.
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2.5x faster, and in memory efficiency, using 0.3x less memory compared to projection-based methods, while achieving competitive performance with only a 0.4% IoU difference.
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</p>
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<h2 class="title is-4" style="text-align: center;">Method Overview</h2>
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<img src="./static/images/Overview.png" alt="model"
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</figure>
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<h2 class="title is-4" style="text-align: center;">Experiment</h2>
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<img src="./static/images/Experiment.png" alt="experiment"
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<img src="./static/images/Experiment.jpg" alt="experiment"
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<figcaption>
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<!-- <p>

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