外观
论文arXiv 2609.044111 个月前
AquaBEV: 基于 3D 声呐监督的单目水下 BEV 占用
原文链接：AquaBEV: Monocular Underwater BEV Occupancy with 3D Sonar Supervision（arXiv 2609.04411 · 发布日期：2026-09-03）
AquaBEV: Monocular Underwater BEV Occupancy with 3D Sonar Supervision
AquaBEV用训练时配对3D成像声呐监督单目RGB图像预测局部水下BEV占用，并在受控基准验证。
核心要点
- 安全导航需要理解周围自由与占用空间，单幅水下RGB因外观几何线索有限且不可靠而困难。
- 方法在训练时利用配对3D成像声呐提供几何监督，从单张RGB图像预测局部BEV占用。
- AquaBEV将视觉特征映射到免标定极坐标表示，沿距离维因果解码后重建笛卡尔BEV。
- 在统一协议的受控水下占用基准上，相比最强迁移基线Visible/Observed IoU相对提升4.0%/4.3%。
验证范围与局限：验证范围为受控水下占用基准中的RGB到声呐任务。
英文摘要 (Abstract)
Autonomous underwater robots are widely used for exploration, monitoring, and inspection, where safe navigation depends on understanding the surrounding free and occupied space. Bird's eye view (BEV) occupancy provides such a representation, but predicting it from a single underwater RGB image is difficult due to limited, unreliable geometric cues from appearance alone. 3D imaging sonar offers complementary geometric measurements to supervise this task. We introduce AquaBEV, a monocular underwater occupancy model that predicts local BEV occupancy from a single RGB image, using paired 3D imaging sonar as geometric supervision during training. AquaBEV maps visual features into a calibration free polar representation and applies causal decoding along the range dimension before reconstructing the prediction in Cartesian BEV coordinates. A controlled underwater occupancy benchmark was established, adapting representative occupancy methods to the same RGB to sonar task under a unified protocol. AquaBEV achieves 31.4 Visible IoU and 38.6 Observed IoU, 4.0% and 4.3% relative improvements over the strongest transferred baseline.
作者：Trung Tien Dong, Shengji Jin, Chen Chen, Yi Sheng, Xiaomin Lin
来源
本页事实对应的公开页面，以原文为准。