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English(EN) WeatherOcc3D: VLM-Assisted Adverse Weather Aware 3D Semantic Occupancy Prediction

WeatherOcc3D利用VLM提升恶劣天气下3D预测能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为WeatherOcc3D的新框架，该框架利用视觉语言模型（VLMs）来改善恶劣天气条件下的3D语义占用预测。该系统利用CLIP的潜在空间和特定于天气的文本嵌入，动态调整摄像头和LiDAR传感器数据的融合。这种自适应方法在晴朗的白天优先考虑摄像头特征，在下雨的夜晚优先考虑LiDAR特征，在nuScenes数据集上的表现显著优于传统的融合方法。 AI

影响 提高了自动驾驶感知系统在恶劣天气条件下的鲁棒性。

排序理由 该集群包含一篇学术论文，详细介绍了一种新的3D语义占用预测方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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WeatherOcc3D利用VLM提升恶劣天气下3D预测能力

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该集群包含一篇学术论文，详细介绍了一种新的3D语义占用预测方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Hasan F. Ates ·

    WeatherOcc3D：VLM辅助的恶劣天气感知3D语义占用预测

    While multi-modal 3D semantic occupancy prediction typically enhances robustness by fusing camera and LiDAR inputs, its effectiveness is fundamentally constrained by environmental variability. Specifically, camera sensors suffer from severe low-light degradation, while LiDAR sens…

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