English(EN) LighTROcc: Lightweight 4D Occupancy Forecasting via Instance-Centric 3D Gaussians

LighTROcc框架推进自动驾驶中的4D占用预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了LighTROcc，一个用于自动驾驶场景中3D占用预测的新型框架。这种实例中心的方法利用学习到的查询和3D高斯来建模可移动物体，并进行单次预测其当前和未来的占用情况。与nuScenes和nuScenes-Occupancy数据集上现有的密集和实例级方法相比，LighTROcc在实例级预测准确性和计算效率方面均有所提高。 AI

影响这个新框架可以提高自动驾驶汽车4D占用预测的准确性和效率。

排序理由这是一篇关于自动驾驶新方法的学术论文。

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Hwanhee Jung, SeungHyeon Kim, Inkyu Koo, Qixing Huang, Sang Ho Yoon, Sangpil Kim · 2026-10-08 04:00

LighTROcc：轻量级实例中心3D高斯用于4D占用预测

arXiv:2610.09444v1 Announce Type: new Abstract: Forecasting future 3D occupancy from surround-view cameras is essential for autonomous driving, yet existing approaches rely on dense voxel or bird's-eye-view representations whose cost grows rapidly with spatial resolution and pred…

本文如何被排名

报道来源 [1]

LighTROcc：轻量级实例中心3D高斯用于4D占用预测

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