LO-World: Unified BEV Latent Modeling for Joint 4D Occupancy and LiDAR Forecasting

Zhimin Liao, Ping Wei, Kai Mao, Shuaijia Chen

LO-World: Unified BEV Latent Modeling for Joint 4D Occupancy and LiDAR Forecasting: 1 upvotes on Hugging Face Daily Papers, #38 of 39 papers on 2026-11-11. Day-by-day upvote history.

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It had 1 upvotes by the next day, 1 upvotes by day 3, 1 upvotes by day 7, and has 1 now. It ranked #126 of 133 papers in its week and #324 of 340 in its month. Its code repository has 5 GitHub stars.

LiDAR point clouds and 3D semantic occupancy represent two distinct modalities capturing fine-grained metric geometry and dense volumetric semantics, respectively, holding natural potential for mutual complementarity. However, existing world models typically forecast these modalities independently or cascade them heuristically, missing the opportunity for reciprocal cross-modal regularization. To address this, we propose LO-World, a unified 4D world model that forecasts both modalities within a shared BEV latent space. This single-stream formulation enables dense semantics and metric geometry to mutually reinforce each other, while eliminating multi-pipeline redundancy. Specifically, LO-World couples an efficient hybrid range-voxel autoencoder with a flow-matching generator that caches historical context to reduce iterative sampling latency. Extensive experiments demonstrate that LO-World outperforms prior state-of-the-art baselines, improving occupancy forecasting IoU by 10.9% and reducing LiDAR Chamfer Distance by 30.1%. Meanwhile, it sustains a real-time throughput exceeding 25~FPS on a single RTX 4090. Our code is available at https://github.com/lzzzzzm/LO-World.

Paper page on Hugging Face · arXiv

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