# RUHSNet: 3D Object Detection Using Lidar Data in Real Time

Source: [RUHSNet: 3D Object Detection Using Lidar Data in Real Time](<https://www.sota2.com/research/paper/68fa6a3e1e209131cea26f4c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:48.550Z

Authors: Abhinav Sagar

Published: 2020-05-09T09:41:46.000Z

## Abstract

In this work, we address the problem of 3D object detection from point cloud data in real time. For autonomous vehicles to work, it is very important for the perception component to detect the real world objects with both high accuracy and fast inference. We propose a novel neural network architecture along with the training and optimization details for detecting 3D objects in point cloud data. We compare the results with different backbone architectures including the standard ones like VGG, ResNet, Inception with our backbone. Also we present the optimization and ablation studies including designing an efficient anchor. We use the Kitti 3D Birds Eye View dataset for benchmarking and validating our results. Our work surpasses the state of the art in this domain both in terms of average precision and speed running at \> 30 FPS. This makes it a feasible option to be deployed in real time applications including self driving cars.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2006.01250>)

[PDF](<https://arxiv.org/pdf/2006.01250.pdf>)