[Submitted on 13 Oct 2026]
Title:Into the LidarVerse: A Real-Time Unified LiDAR World Model Across Tasks and Sensors
View a PDF of the paper titled Into the LidarVerse: A Real-Time Unified LiDAR World Model Across Tasks and Sensors, by Henry Che and 5 other authors
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Abstract:We present LidarVerse, a real-time unified LiDAR world model for sensor simulation across tasks and sensors. Existing LiDAR simulators largely follow one of two complementary paradigms: reconstruction-based methods preserve the geometry of a captured scene but require lengthy per-scene optimization, whereas generative methods amortize learning across data but lack the consistency needed for long-horizon rollouts. LidarVerse brings both paradigms into a single, unified conditional generative framework. A universal tokenizer maps LiDAR range sequences into a shared latent space, where a bidirectional teacher learns a common interface for conditioning on a LiDAR prefix, an HD-Map, a persistent spatial cache, and optionally, text. Varying the available scene evidence turns the same model into a generator of new worlds, a point-cloud forecaster, or a generalizable resimulator of observed scenes without per-scene optimization. We distill the teacher into a four-step autoregressive causal student and maintain both a rolling temporal K/V cache and a world-aligned spatial cache, enabling persistent long-horizon rollouts with real-time inference at 11 FPS on a single H100 GPU. LidarVerse achieves state-of-the-art performance on most reported metrics across all three evaluation protocols, while its tokenizer supports 32-, 64-, and 128-beam LiDAR and generalizes to an unseen scanning pattern. These results demonstrate that generative and reconstruction-grounded LiDAR simulation can be realized within one model, providing the core capabilities for future closed-loop sensor simulation.
Comments: 31 pages, 17 figures. Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
Cite as: arXiv:2610.19201 [cs.CV]
(or arXiv:2610.19201v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.19201
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Henry Che [view email] [v1] Tue, 13 Oct 2026 21:11:28 UTC (46,136 KB)
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