SparseWorld: A Flexible, Adaptive, and Efficient 4D Occupancy World Model Powered by Sparse and Dynamic Queries

TL;DR

SparseWorld introduces a novel 4D occupancy world model that employs sparse, dynamic queries and modules for perception and forecasting, achieving state-of-the-art results in perception, forecasting, and planning tasks.

Contribution

It presents a flexible, adaptive, and efficient 4D occupancy model with novel modules for range-adaptive perception and state-conditioned forecasting, addressing limitations of static grid-based models.

Findings

Achieves state-of-the-art performance in perception, forecasting, and planning.

Demonstrates enhanced flexibility and adaptability over traditional models.

Validates effectiveness through extensive experiments and ablation studies.

Abstract

Semantic occupancy has emerged as a powerful representation in world models for its ability to capture rich spatial semantics. However, most existing occupancy world models rely on static and fixed embeddings or grids, which inherently limit the flexibility of perception. Moreover, their ``in-place classification" over grids exhibits a potential misalignment with the dynamic and continuous nature of real scenarios. In this paper, we propose SparseWorld, a novel 4D occupancy world model that is flexible, adaptive, and efficient, powered by sparse and dynamic queries. We propose a Range-Adaptive Perception module, in which learnable queries are modulated by the ego vehicle states and enriched with temporal-spatial associations to enable extended-range perception. To effectively capture the dynamics of the scene, we design a State-Conditioned Forecasting module, which replaces classification-based forecasting with regression-guided formulation, precisely aligning the dynamic queries with the continuity of the 4D environment. In addition, We specifically devise a Temporal-Aware Self-Scheduling training strategy to enable smooth and efficient training. Extensive experiments demonstrate that SparseWorld achieves state-of-the-art performance across perception, forecasting, and planning tasks. Comprehensive visualizations and ablation studies further validate the advantages of SparseWorld in terms of flexibility, adaptability, and efficiency.