Monocular 3D detectors achieve remarkable performance on cars and smaller
objects. However, their performance drops on larger objects, leading to fatal
accidents. Some attribute the failures to training data scarcity or their
receptive field requirements of large objects. In this paper, we highlight this
understudied problem of generalization to large objects. We find that modern
frontal detectors struggle to generalize to large objects even on nearly
balanced datasets. We argue that the cause of failure is the sensitivity of
depth regression losses to noise of larger objects. To bridge this gap, we
comprehensively investigate regression and dice losses, examining their
robustness under varying error levels and object sizes. We mathematically prove
that the dice loss leads to superior noise-robustness and model convergence for
large objects compared to regression losses for a simplified case. Leveraging
our theoretical insights, we propose SeaBird (Segmentation in Bird's View) as
the first step towards generalizing to large objects. SeaBird effectively
integrates BEV segmentation on foreground objects for 3D detection, with the
segmentation head trained with the dice loss. SeaBird achieves SoTA results on
the KITTI-360 leaderboard and improves existing detectors on the nuScenes
leaderboard, particularly for large objects. Code and models at
this https URL

该研究论文研究了单目 3D 检测器在较大物体上的泛化问题，发现深度回归损失对更大物体的噪声敏感性导致性能下降。通过理论分析，提出了一种名为 SeaBird 的方法，结合了 BEV 分割和 dice 损失，实现了较大物体的泛化和 SoTA 结果。

SeaBird: 基于 Dice 损失的鸟瞰分割方法改善大物体的单目三维检测

SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular  3D Detection of Large Objects

Object detection models, a prominent class of machine learning algorithms,
aim to identify and precisely locate objects in images or videos. However, this
task might yield uneven performances sometimes caused by the objects sizes and
the quality of the images and labels used for training. In this paper, we
highlight the importance of large objects in learning features that are
critical for all sizes. Given these findings, we propose to introduce a
weighting term into the training loss. This term is a function of the object
area size. We show that giving more weight to large objects leads to improved
detection scores across all object sizes and so an overall improvement in
Object Detectors performances (+2 p.p. of mAP on small objects, +2 p.p. on
medium and +4 p.p. on large on COCO val 2017 with InternImage-T). Additional
experiments and ablation studies with different models and on a different
dataset further confirm the robustness of our findings.

给出了一个在目标检测中引入加权项的方法，加权项是一个关于对象面积大小的函数，实验证明给较大的对象赋予更高的权重能够提高所有对象尺寸下的检测分数及目标探测性能。