LiDAR Snowfall Simulation for Robust 3D Object Detection (2203.15118v2)

Abstract: 3D object detection is a central task for applications such as autonomous driving, in which the system needs to localize and classify surrounding traffic agents, even in the presence of adverse weather. In this paper, we address the problem of LiDAR-based 3D object detection under snowfall. Due to the difficulty of collecting and annotating training data in this setting, we propose a physically based method to simulate the effect of snowfall on real clear-weather LiDAR point clouds. Our method samples snow particles in 2D space for each LiDAR line and uses the induced geometry to modify the measurement for each LiDAR beam accordingly. Moreover, as snowfall often causes wetness on the ground, we also simulate ground wetness on LiDAR point clouds. We use our simulation to generate partially synthetic snowy LiDAR data and leverage these data for training 3D object detection models that are robust to snowfall. We conduct an extensive evaluation using several state-of-the-art 3D object detection methods and show that our simulation consistently yields significant performance gains on the real snowy STF dataset compared to clear-weather baselines and competing simulation approaches, while not sacrificing performance in clear weather. Our code is available at www.github.com/SysCV/LiDAR_snow_sim.

• The paper introduces a physics-based simulation that models individual snowflake interactions and wet ground effects on LiDAR returns.
• It employs Fresnel equations and thin surface reflection principles to mimic realistic snowfall impacts on intensity and range data.
• Models trained with the simulated data yield up to 2.1% AP improvement in snowy conditions without losing clear-weather performance.

LiDAR Snowfall Simulation for Robust 3D Object Detection

The paper "LiDAR Snowfall Simulation for Robust 3D Object Detection," authored by Hahner et al., addresses a pertinent challenge in the field of autonomous driving—3D object detection using LiDAR sensors under adverse weather conditions, specifically, snowfall. While LiDAR systems typically perform well under clear conditions, their efficacy is compromised by factors such as rain, fog, and snow due to their interaction with atmospheric particles. This paper presents a rigorous approach to tackle the snow-induced degradation of LiDAR point clouds, enhancing the robustness of 3D object detection models in snowy environments.

Contributions and Methodology

The authors introduce a methodology to simulate snow effects on LiDAR data, providing an alternative to collecting extensive snowy condition datasets, which are prohibitive in cost and effort. The proposed simulation physically models the impact of snowflakes and wet surfaces on LiDAR measurements. By sampling individual snow particles as opaque spheres and modeling their interaction with LiDAR beams, the simulation adjusts the intensity and range of LiDAR returns to replicate real-world snowy conditions. Furthermore, the authors integrate a ground wetness model, employing Fresnel equations and thin surface reflection principles to simulate wet ground effects, resulting in realistic alterations of LiDAR intensity values from ground surfaces.

Experimental Validation

The paper employs the STF dataset for evaluation, rigorously testing state-of-the-art 3D object detection methods, such as PV-RCNN, VoxelRCNN-Car, and CenterPoint. The research demonstrates that models trained with the simulated snowy LiDAR data show consistent improvements in average precision (AP) by up to 2.1% compared to clear-weather baselines when evaluated on samples featuring heavy snowfall. Notably, these improvements are achieved without sacrificing performance in non-snowy scenarios, thereby supporting the generalizability of the trained models across diverse weather conditions. A significant finding is that denoising methods like DROR and competing simulation techniques, which are effective in other conditions such as fog, do not perform as well in snowy environments, highlighting the importance of the physics-based approach tailored to snow.

Implications and Future Research

The implications of this work are twofold: practically, it offers an efficient solution for enhancing the reliability and safety of autonomous vehicles in snowy climates; theoretically, it contributes to the understanding of LiDAR signal processing in scattering environments. The paper provides robust foundations for further exploration into adverse weather simulations and their integration into autonomous driving systems.

For future research, several avenues can be pursued. The exploration of temporal cues in LiDAR data could further enhance object detection accuracy during dynamic snow events. Additionally, extending the simulation framework to support other adverse conditions and integrating multi-sensor fusion approaches may yield further improvements for autonomous subsystem reliability.

Overall, this paper presents a compelling solution to a practical problem, making significant strides towards ensuring robust autonomous driving capabilities under snowfall conditions through a methodologically sound and practically viable approach.