提升单目3D检测对未知摄像头高度的鲁棒性，性能提升超45%。
CHARM3R: Towards Unseen Camera Height Robust Monocular 3D Detector
- 融合回归与地面基准深度估计，减少摄像头高度变化影响
- 在多高度CARLA数据集上实现超过45%的泛化性能提升
- 适合需要跨场景部署的自动驾驶感知系统
单目3D目标检测器在特定车载摄像头高度下表现良好，但在未知或分布外的高度下性能显著下降。现有方法常依赖Plucker嵌入、图像变换或数据增强。本文首次系统分析了摄像头高度变化对主流单目3D模型的影响。基于扩展版CARLA数据集（含多种摄像头高度），我们发现深度估计是影响性能的关键因素。数学证明并实证观察到：回归型深度模型的平均深度误差随高度变化呈一致负趋势，而基于地面的深度模型则呈正趋势。为此，提出相机高度鲁棒的单目3D检测器CHARM3R，通过模型内融合两种深度估计实现稳定性提升。CHARM3R在未见摄像头高度下性能提升超过45%，在CARLA数据集上达到最新水平。代码与模型详见https://github.com/abhi1kumar/CHARM3R。
原文摘要 · Abstract (English)
Monocular 3D object detectors, while effective on data from one ego camera height, struggle with unseen or out-of-distribution camera heights. Existing methods often rely on Plucker embeddings, image transformations or data augmentation. This paper takes a step towards this understudied problem by first investigating the impact of camera height variations on state-of-the-art (SoTA) Mono3D models. With a systematic analysis on the extended CARLA dataset with multiple camera heights, we observe that depth estimation is a primary factor influencing performance under height variations. We mathematically prove and also empirically observe consistent negative and positive trends in mean depth error of regressed and ground-based depth models, respectively, under camera height changes. To mitigate this, we propose Camera Height Robust Monocular 3D Detector (CHARM3R), which averages both depth estimates within the model. CHARM3R improves generalization to unseen camera heights by more than $45\%$, achieving SoTA performance on the CARLA dataset. Codes and Models at https://github.com/abhi1kumar/CHARM3R
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