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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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