# Leveraging Temporal Information for 3D Detection and Domain Adaptation

arXiv: https://arxiv.org/abs/2006.16796 · HTML: https://mlsanity.com/paper/2006.16796/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection](https://mlsanity.com/paper/2208.03141/) (2022)

## Abstract (arXiv)

Ever since the prevalent use of the LiDARs in autonomous driving, tremendous improvements have been made to the learning on the point clouds. However, recent progress largely focuses on detecting objects in a single 360-degree sweep, without extensively exploring the temporal information. In this report, we describe a simple way to pass such information in the learning pipeline by adding timestamps to the point clouds, which shows consistent improvements across all three classes.