Bounded by the inherent ambiguity of depth perception, contemporary
camera-based 3D object detection methods fall into the performance bottleneck.
Intuitively, leveraging temporal multi-view stereo (MVS) technology is the
natural knowledge for tackling this ambiguity. However, traditional attempts of
MVS are flawed in two aspects when applying to 3D object detection scenes: 1)
The affinity measurement among all views suffers expensive computation cost; 2)
It is difficult to deal with outdoor scenarios where objects are often mobile.
To this end, we introduce an effective temporal stereo method to dynamically
select the scale of matching candidates, enable to significantly reduce
computation overhead. Going one step further, we design an iterative algorithm
to update more valuable candidates, making it adaptive to moving candidates. We
instantiate our proposed method to multi-view 3D detector, namely BEVStereo.
BEVStereo achieves the new state-of-the-art performance (i.e., 52.5% mAP and
61.0% NDS) on the camera-only track of nuScenes dataset. Meanwhile, extensive
experiments reflect our method can deal with complex outdoor scenarios better
than contemporary MVS approaches. Codes have been released at
this https URL

本文提出了一种基于时间立体的有效方法来处理三维物体检测中深度感知所固有的模糊性。其有效地选择了匹配候选项的比例，极大地减少了计算开销，设计了一种迭代算法来更新更有价值的候选项，使其适应于移动的候选项，并达到了比现有的 MVS 方法更好的性能。

BEVStereo：动态时间立体下的多视角三维物体检测深度估计增强

BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection  with Dynamic Temporal Stereo

Learning accurate depth is essential to multi-view 3D object detection.
Recent approaches mainly learn depth from monocular images, which confront
inherent difficulties due to the ill-posed nature of monocular depth learning.
Instead of using a sole monocular depth method, in this work, we propose a
novel Surround-view Temporal Stereo (STS) technique that leverages the geometry
correspondence between frames across time to facilitate accurate depth
learning. Specifically, we regard the field of views from all cameras around
the ego vehicle as a unified view, namely surroundview, and conduct temporal
stereo matching on it. The resulting geometrical correspondence between
different frames from STS is utilized and combined with the monocular depth to
yield final depth prediction. Comprehensive experiments on nuScenes show that
STS greatly boosts 3D detection ability, notably for medium and long distance
objects. On BEVDepth with ResNet-50 backbone, STS improves mAP and NDS by 2.6%
and 1.4%, respectively. Consistent improvements are observed when using a
larger backbone and a larger image resolution, demonstrating its effectiveness

利用 Surround-view Temporal Stereo 技术结合单眼深度算法实现深度学习，通过时间轴上的帧之间的几何对应来提高准确性，从而显著提高 3D 检测能力。