# 3D Roadway Scene Object Detection with LIDARs in Snowfall Conditions

arXiv: https://arxiv.org/abs/2510.22436 · HTML: https://mlsanity.com/paper/2510.22436/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Spherical Transformer for LiDAR-based 3D Recognition](https://mlsanity.com/paper/2303.12766/) (2023)
- [Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather](https://mlsanity.com/paper/2108.05249/) (2021)
- [LiDAR Snowfall Simulation for Robust 3D Object Detection](https://mlsanity.com/paper/2203.15118/) (2022)
- [Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection](https://mlsanity.com/paper/2107.07004/) (2021)
- [A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?](https://mlsanity.com/paper/1912.03251/) (2019)

## Built on by (most cited first, 2 of 2)

- [Geometry vs Structure: Graph-Based Diagnostics for LiDAR Point-Cloud Simulation Fidelity](https://mlsanity.com/paper/2609.16378/) (2026)
- [Beyond the Snowflakes: Disentangling Weather Effects from Confounding Meta-Factors in LiDAR Object Detection](https://mlsanity.com/paper/2610.13891/) (2026)

## Abstract (arXiv)

Because 3D structure of a roadway environment can be characterized directly by a Light Detection and Ranging (LiDAR) sensors, they can be used to obtain exceptional situational awareness for assitive and autonomous driving systems. Although LiDARs demonstrate good performance in clean and clear weather conditions, their performance significantly deteriorates in adverse weather conditions such as those involving atmospheric precipitation. This may render perception capabilities of autonomous systems that use LiDAR data in learning based models to perform object detection and ranging ineffective. While efforts have been made to enhance the accuracy of these models, the extent of signal degradation under various weather conditions remains largely not quantified. In this study, we focus on the performance of an automotive grade LiDAR in snowy conditions in order to develop a physics-based model that examines failure modes of a LiDAR sensor. Specifically, we investigated how the LiDAR signal attenuates with different snowfall rates and how snow particles near the source serve as small but efficient reflectors. Utilizing our model, we transform data from clear conditions to simulate snowy scenarios, enabling a comparison of our synthetic data with actual snowy conditions. Furthermore, we employ this synthetic data, representative of different snowfall rates, to explore the impact on a pre-trained object detection model, assessing its performance under varying levels of snowfall