In the intersection of computer vision and robotic perception, 4D reconstruction of dynamic scenes serve as the critical bridge connecting low-level geometric sensing with high-level semantic understanding. We present DINO\_4D, introducing frozen DINOv3 features as structural priors, injecting semantic awareness into the reconstruction process to effectively suppress semantic drift during dynamic tracking. Experiments on the Point Odyssey and TUM-Dynamics benchmarks demonstrate that our method maintains the linear time complexity O(T) of its predecessors while significantly improving Tracking Accuracy (APD) and Reconstruction Completeness. DINO\_4D establishes a new paradigm for constructing 4D World Models that possess both geometric precision and semantic understanding.
@article{arxiv.2604.09877,
title = {DINO_4D: Semantic-Aware 4D Reconstruction},
author = {Yiru Yang and Zhuojie Wu and Quentin Marguet and Nishant Kumar Singh and Max Schulthess},
journal= {arXiv preprint arXiv:2604.09877},
year = {2026}
}