INT: Towards Infinite-frames 3D Detection with An Efficient Framework

Jianyun Xu, Zhenwei Miao, Da Zhang, Hongyu Pan, Kai Liu, Peihan Hao, Jun Zhu, Zhengyang Sun, Hongming Li, Xin Zhan
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引用次数: 7

Abstract

It is natural to construct a multi-frame instead of a single-frame 3D detector for a continuous-time stream. Although increasing the number of frames might improve performance, previous multi-frame studies only used very limited frames to build their systems due to the dramatically increased computational and memory cost. To address these issues, we propose a novel on-stream training and prediction framework that, in theory, can employ an infinite number of frames while keeping the same amount of computation as a single-frame detector. This infinite framework (INT), which can be used with most existing detectors, is utilized, for example, on the popular CenterPoint, with significant latency reductions and performance improvements. We've also conducted extensive experiments on two large-scale datasets, nuScenes and Waymo Open Dataset, to demonstrate the scheme's effectiveness and efficiency. By employing INT on CenterPoint, we can get around 7% (Waymo) and 15% (nuScenes) performance boost with only 2~4ms latency overhead, and currently SOTA on the Waymo 3D Detection leaderboard.
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INT:用一个有效的框架实现无限帧3D检测
对于连续时间流,构建多帧而不是单帧3D检测器是很自然的。虽然增加帧数可能会提高性能,但由于计算和内存成本的显著增加,以前的多帧研究只使用非常有限的帧来构建系统。为了解决这些问题,我们提出了一种新的流上训练和预测框架,理论上,它可以使用无限数量的帧,同时保持与单帧检测器相同的计算量。这种无限框架(INT)可以与大多数现有的检测器一起使用,例如,在流行的CenterPoint上使用,可以显著减少延迟并提高性能。我们还在nuScenes和Waymo Open Dataset这两个大型数据集上进行了广泛的实验,以证明该方案的有效性和效率。通过在CenterPoint上使用INT,我们可以在只有2~4ms延迟开销的情况下获得7% (Waymo)和15% (nuScenes)的性能提升,目前在Waymo 3D检测排行榜上排名SOTA。
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