The KITTI Vision Benchmark Suite

Modern 3D object detectors have immensely benefited from the end-to-end learning idea. However, most of them use a post-processing algorithm called Non-Maximal Suppression (NMS) only during inference. In this paper, we present and integrate GrooMeD-NMS -- a novel Grouped Mathematically Differentiable NMS for monocular 3D object detection, such that the network is trained end-to-end with a loss on the boxes after NMS. We first formulate NMS as a matrix operation and then group and mask the boxes in an unsupervised manner to obtain a simple closed-form expression of the NMS. GrooMeD-NMS addresses the mismatch between training and inference pipelines and, therefore, forces the network to select the best 3D box in a differentiable manner. As a result, GrooMeD-NMS achieves state-of-the-art monocular 3D object detection results on the KITTI benchmark dataset performing comparably to monocular video-based methods.

N_t = 0.4, \alpha = 100, \beta = 0.3

@inproceedings{kumar2021groomed,
title={GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection},
author={Kumar, Abhinav and Brazil, Garrick and Liu, Xiaoming},
booktitle={CVPR},
year={2021}
}