OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation

2026-05-13 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

OmniLiDAR is a unified text-conditioned diffusion framework designed for generating 3D LiDAR scans across diverse sensing conditions. It addresses the limitation of single-domain LiDAR generators by synthesizing data in a shared range-image representation across eight distinct domains, encompassing adverse weather, sensor configuration changes (e.g., reduced beams), and cross-platform acquisition (vehicle, drone, quadruped). The framework introduces a Cross-Domain Training Strategy (CDTS) to mix domains within mini-batches and uses conditioning to guide generation. Additionally, it incorporates Cross-Domain Feature Modeling (CDFM) to capture anisotropic scanning structures and Domain-Adaptive Feature Scaling (DAFS) for modulating domain-dependent feature shifts. To facilitate development, the authors constructed an 8-domain dataset by combining real-world scans with physically based weather simulations and systematic beam reduction. Experiments confirm high generation fidelity and consistent improvements in downstream tasks like LiDAR semantic segmentation and 3D object detection, particularly in limited-label scenarios.

Key takeaway

For research scientists developing autonomous driving or robotics systems, OmniLiDAR offers a robust solution for generating diverse 3D LiDAR data. You can use this framework to create synthetic datasets that span multiple sensing conditions and platforms, significantly reducing the cost and effort of real-world data collection. This approach improves model robustness and performance, especially in scenarios with limited labeled data, by providing high-fidelity, domain-adaptive synthetic examples.

Key insights

OmniLiDAR unifies 3D LiDAR generation across diverse sensing domains using a text-conditioned diffusion framework.

Principles

• Unified models can handle heterogeneous distribution shifts.
• Anisotropic scanning structures require specialized feature modeling.
• Domain mixing improves generalization in multi-domain training.

Method

OmniLiDAR employs a Cross-Domain Training Strategy (CDTS) with mini-batch domain mixing, Cross-Domain Feature Modeling (CDFM) for anisotropic structures, and Domain-Adaptive Feature Scaling (DAFS) for feature modulation during denoising.

In practice

• Generate synthetic LiDAR data for diverse weather conditions.
• Simulate various sensor configurations (e.g., reduced beams).
• Augment datasets for 3D object detection and segmentation.

Topics

• LiDAR Generation
• Diffusion Models
• Multi-Domain Synthesis
• Cross-Domain Training Strategy
• Range-Image Representation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.