GrooMeD NMS- Grouped Mathematical Differentiable NMS for Monocular 3D Object Detection

­ Monocular 3D Object Detection   VALUE PROPOSITION   This technology is a novel end-to-end trainable Grouped Mathematical Differentiable NMS (GrooMed-NMS) for monocular 3D object detection.   INTRODUCTION Modern 3D object d…

Monocular 3D Object Detection

VALUE PROPOSITION

This technology is a novel end-to-end trainable Grouped Mathematical Differentiable NMS (GrooMed-NMS) for monocular 3D object detection.

INTRODUCTION

Modern 3D object detection methods have immensely benefitted from the end-to-end learning idea. However, most of them use a post-processing algorithm called Non-Maximal Suppression (NMS) only during inference. Although there have been attempts to include NMS in the training pipeline for other tasks such as 2D object detection, they have been less widely adopted due to a non-mathematical expression of the NMS.

DESCRIPTION OF TECHNOLOGY

This technology formulates NMS as a matrix operation and then groups and masks 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 box in a differentiable manner. GrooMeDNMS achieves state-of-the-art monocular 3D object detection results on the KITTI dataset performing comparably to monocular video methods.

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Monocular 3D Object Detection

VALUE PROPOSITION

This technology is a novel end-to-end trainable Grouped Mathematical Differentiable NMS (GrooMed-NMS) for monocular 3D object detection.

INTRODUCTION

Modern 3D object detection methods have immensely benefitted from the end-to-end learning idea. However, most of them use a post-processing algorithm called Non-Maximal Suppression (NMS) only during inference. Although there have been attempts to include NMS in the training pipeline for other tasks such as 2D object detection, they have been less widely adopted due to a non-mathematical expression of the NMS.

DESCRIPTION OF TECHNOLOGY

This technology formulates NMS as a matrix operation and then groups and masks 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 box in a differentiable manner. GrooMeDNMS achieves state-of-the-art monocular 3D object detection results on the KITTI dataset performing comparably to monocular video methods.

BENEFITS

This technology enables an end-to-end trainable closed-form mathematical differential Non-Maximal Suppression.
GrooMeD-NMS addresses the mismatch between training and inference pipelines prevalent in monocular 3D object detection and therefore forces the network to select the best box in a differentiable manner.
APPLICATIONS

GrooMeD-NMS is generic and can be applied to many different object detection tasks.
This method can be applied to tasks such as LiDAR-based 3D object detection and pedestrian detection.

PATENT STATUS

  • Patent pending

LICENSING RIGHTS

  • Limited licensing rights available

INVENTORS

  • Abhinav Kumar, Garrick Brazil, and Xiaoming Liu

TECH ID

  • TEC2021-0036

Website

http://msut.technologypublisher.com/technology/46592

Contact Information

TTO Home Page: http://msut.technologypublisher.com

Name: Raymond Devito

Title: Technology Manager

Department: MSU Technologies

Email: devitora@msu.edu