ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions

Vivek Anand; Bharat Lohani; Rakesh Mishra; Gaurav Pandey

cs.ROApr 11, 2026

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions

Authors: Vivek Anand, Bharat Lohani, Rakesh Mishra, Gaurav Pandey

Organizations: Geoinformatics Group, Department of Civil Engineering, Indian Institute of Technology Kanpur, India · Department of Geodesy and Geomatics Engineering, University of New Brunswick, NB, Canada · Department of Engineering Technology & Industrial Distribution (ETID) of Texas A&M University

Abstract

Reliable LiDAR perception requires robustness across sensors, environments, and adverse weather. However, existing datasets rarely provide physically consistent observations of the same scene under varying sensor configurations and weather conditions, limiting systematic analysis of domain shifts. This work presents ReaLiTy, a unified physics-informed framework that transforms LiDAR data to match target sensor specifications and weather conditions. The framework integrates physically grounded cues with a learning-based module to generate realistic intensity patterns, while a physics-based weather model introduces consistent geometric and radiometric degradations. Building on this framework, we introduce the LiDAR Adaptation Dataset Suite (LADS), a collection of physically consistent, transformation-ready point clouds with one-to-one correspondence to original datasets. Experiments demonstrate improved cross-domain consistency and realistic weather effects. ReaLiTy and LADS provide a reproducible foundation for studying LiDAR adaptation and simulation-driven perception in intelligent transportation systems.

Figures & tables

Fig. 1 : Overview of the proposed ReaLiTy framework. A modular design with physics-guided sensor and weather adaptation branches driven by shared physical cues such as range, incidence angle, and material reflectance.

Source	Frames Used	Variants Provided
SemanticKITTI	Sequences 00–10	Snow, Rain
nuScenes lidarseg	All labeled keyframes	Snow, Rain
Voxelscape	Sequences 00–10	Snow, Rain, Intensity-adapted

TABLE I : Released LADS adverse-weather LiDAR data and corresponding source frames. The original file and folder structure is preserved for direct reuse of calibration and label files.

Fig. 2 : Qualitative Analysis: Visual assessment of results from our ReaLiTy framework trained on the SemanticKITTI dataset. a) Physics-modelled simulated LiDAR intensity; b) LiDAR intensity produced by our PICGAN architecture with domain adaptation from the SemanticKITTI dataset; c) Sample LiDAR intensity from the SemanticKITTI dataset. Specific objects are annotated to aid visual comparison: A depicts cars, B depicts pedestrians, C depicts lane markings, and D depicts poles.

Fig. 3 : Qualitative Analysis: Visual assessment of results from our ReaLiTy framework trained on the nuScenes dataset. a) Physics-modelled LiDAR intensity image; b) LiDAR intensity image produced by our PICGAN architecture with domain adaptation from nuScenes dataset; c) Sample LiDAR intensity image from nuScenes dataset. Specific objects are annotated to aid visual comparison: A depicts cars, B depicts pedestrians, C depicts lane markings, and D depicts poles.

Fig. 4 : Qualitative Analysis: Visual assessment of results from our ReaLiTy framework for snow weather. a) Original KITTI Clear weather data; b) KITTI-Snow weather data produced by our ReaLiTy Framework; c) Reference snow weather data from the CADC dataset.

Fig. 5 : Qualitative Analysis: Visual assessment of results from our ReaLiTy framework for rain weather. a) Original nuScenes Clear weather data; b) nuScenes-Rain weather data produced by our ReaLiTy framework; c) Reference rain weather data from the Boreas dataset.

Condition	Method	Training Data	Total Frames
Snow	Baseline	KITTI-Clear	4000
		KITTI-Clear + CADC	2000 + 2000
		CADC (Real Snow)	4000
	Proposed	KITTI-Snow (Ours)	4000
	Proposed	KITTI-Snow (Ours) + CADC	2000 + 2000
Snow	Baseline	nuScenes-Clear	4000

TABLE II : Training Data Combinations for Snow Condition

KITTI $\rightarrow$ Snow Evaluation (Tested on CADC Real Snow)
Class	Training Data	3D (0.7)	3D (0.5)	BEV (0.7)	BEV (0.5)	Notes
Car	KITTI-Clear	6.8	14.9	7.9	18.4	Poor snow generalization
	KITTI-Snow (Ours)	28.4	36.1	30.2	41.8	Synthetic snow improves robustness
	KITTI-Clear + CADC	32.6	39.4	34.1	45.8	Real snow data improves robustness
	KITTI-Snow (Ours) + CADC	39.5	46.2	41.8	49.9	Best synthetic+real combination
	CADC (Real Snow)	47.9	53.8	49.6	56.9	Real-snow upper bound

TABLE III : PV-RCNN object detection performance under snow conditions using different training dataset combinations for KITTI and nuScenes. Synthetic snow generated by our ReaLiTy framework significantly improves robustness, especially when combined with real CADC snow data.

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Simulating Realistic LiDAR Data Under Adverse Weather for Autonomous Vehicles: A Physics-Informed Learning Approach

Vivek Anand, Bharat Lohani, Rakesh Mishra, Gaurav Pandey

Geoinformatics Group, Department of Civil Engineering, Indian Institute of Technology Kanpur, India · Department of Geodesy and Geomatics Engineering, University of New Brunswick, NB, Canada · Department of Engineering Technology & Industrial Distribution (ETID) of Texas A&M University

Autonomous Driving Simulation Synthetic Data Augmentation Physics-Informed Generative Modeling

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Accurate LiDAR simulation is crucial for autonomous driving, especially under adverse weather conditions. Existing methods struggle to capture the complex interactions between LiDAR signals and atmospheric phenomena, leading to unrealistic representations. This paper presents a physics-informed learning framework (PICWGAN) for generating realistic LiDAR data under adverse weather conditions. By integrating physicsdriven constraints for modeling signal attenuation and geometryconsistent degradations into a physics-informed learning pipeline, the proposed method reduces the sim-to-real gap. Evaluations on real-world datasets (CADC for snow, Boreas for rain) and the VoxelScape dataset show that our approach closely mimics realworld intensity patterns. Quantitative metrics, including MSE, SSIM, KL divergence, and Wasserstein distance, demonstrate statistically consistent intensity distributions. Additionally, models trained on data enhanced by our framework outperform baselines in downstream 3D object detection, achieving performance comparable to models trained on real-world data. These results highlight the effectiveness of the proposed approach in improving the realism of LiDAR data and enabling robust perception under adverse weather conditions.

Abstract

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