TL;DR提出了一种新的基于对象的框架来进行基于学习的立体 3D 物体检测,该模型可以在考虑场景中的显著变化的情况下,利用局部更新问题进行精细定位,并构建了一个粗到细的多分辨率系统,实现模型无关的物体位置细化,将跟踪实现在检测上。在 KITTI 基准测试中取得了最先进的性能。
Abstract
We propose a new object-centric framework for learning-based stereo 3D object
detection. Previous studies build scene-centric representations that do not
consider the significant variation among outdoor instances and thus lack the
flexibility and functionalities that an instance-level model