Towards Real-Time Full-Waveform LiDAR Transformers via Intensity-Guided Token Reduction and Physics-Aware Augmentation
The paper proposes FWL-ToPM, a token pruning and merging mechanism that uses intensity distributions to reduce tokens in full-waveform LiDAR Transformers, and FWLAug, a waveform-preserving augmentation that retains each ray's…
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- Primary sourceHugging Face
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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The paper proposes FWL-ToPM, a token pruning and merging mechanism that uses intensity distributions to reduce tokens in full-waveform LiDAR Transformers, and FWLAug, a waveform-preserving augmentation that retains each ray's Time-of-Flight waveform and frustum geometry. The authors report over 8x speedup at real-time throughput and nearly 9-point F1 gain on the Ghost-FWL benchmark over the prior state of the art, plus improved performance on an out-of-distribution test set of unseen scenes. Code and the OOD dataset are stated to be publicly available.
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Source claims not independently checked; results are as reported by the authors on the Ghost-FWL benchmark and their own OOD set, and the full paper was not verified beyond the supplied page text.
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Source claims not independently checked; results are as reported by the authors on the Ghost-FWL benchmark and their own OOD set, and the full paper was not verified beyond the supplied page text.
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FWL-ToPM uses intensity distributions to selectively prune and merge tokens while retaining critical waveform content.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseOn Ghost-FWL, FWL-ToPM reportedly achieves over 8x speedup at real-time throughput and nearly 9-point F1 gain over prior work.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseFWLAug preserves per-ray Time-of-Flight waveforms and frustum geometry, and reportedly improves results on standard and out-of-distribution test sets.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseLimits from the stored brief
Source claims not independently checked; results are as reported by the authors on the Ghost-FWL benchmark and their own OOD set, and the full paper was not verified beyond the supplied page text.
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Why this may matter
Full-waveform LiDAR can perceive through fog, rain, and transparent or low-reflectance surfaces, but Transformer models have been too costly for real-time use. Reported gains in speed and generalization suggest a path toward practical deployment in autonomous driving and robotics.
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Source claims not independently checked; results are as reported by the authors on the Ghost-FWL benchmark and their own OOD set, and the full paper was not verified beyond the supplied page text.
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