# Enhancing Lidar-based Object Detection in Adverse Weather using Offset Sequences in Time

Source: [Enhancing Lidar-based Object Detection in Adverse Weather using Offset Sequences in Time](<https://www.sota2.com/research/paper/68fa71df1e209131cea537b2>)

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

Data updated: 2025-10-26T02:14:27.865Z

Authors: Raphael van Kempen, Tim Rehbronn, Abin Jose, Johannes Stegmaier, Bastian Lampe, Timo Woopen, Lutz Eckstein

Published: 2024-01-17T08:31:58.000Z

## Abstract

Automated vehicles require an accurate perception of their surroundings for safe and efficient driving. Lidar-based object detection is a widely used method for environment perception, but its performance is significantly affected by adverse weather conditions such as rain and fog. In this work, we investigate various strategies for enhancing the robustness of lidar-based object detection by processing sequential data samples generated by lidar sensors. Our approaches leverage temporal information to improve a lidar object detection model, without the need for additional filtering or pre-processing steps. We compare $10$ different neural network architectures that process point cloud sequences including a novel augmentation strategy introducing a temporal offset between frames of a sequence during training and evaluate the effectiveness of all strategies on lidar point clouds under adverse weather conditions through experiments. Our research provides a comprehensive study of effective methods for mitigating the effects of adverse weather on the reliability of lidar-based object detection using sequential data that are evaluated using public datasets such as nuScenes, Dense, and the Canadian Adverse Driving Conditions Dataset. Our findings demonstrate that our novel method, involving temporal offset augmentation through randomized frame skipping in sequences, enhances object detection accuracy compared to both the baseline model (Pillar-based Object Detection) and no augmentation.

## 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/2401.09049>)

[PDF](<https://arxiv.org/pdf/2401.09049.pdf>)