Vision-language models still struggle to understand 3D spatial relations across views and update spatial judgments as the observer moves. A common approach fuses 3D reconstruction features into visual tokens, but aligning these features with pretrained semantic representations remains challenging under limited spatial...
Han Su, Meng Tian, Tian-Yu Huang et al.· 0 citations
Across seven representative adapted baselines, Grounding2Route substantially outperforms existing methods in all metrics, and a substantial gap to human demonstrations remains, highlighting the difficulty of Map2Route and the considerable headroom for future progress.
4D-WAM is presented, a model that leverages geometric foundation models for training-time supervision to enable 4D consistent world modeling and effectively models 4D consistent scene evolution and achieves state-of-the-art performance on challenging NAVSIM-v1 and NAVSIM-v2 benchmarks.
This work introduces SUV, a unified end-to-end driving framework that casts future Scene Understanding as Video generation using a pretrained video foundation model, and shows that structured future supervision and direct future-stream access yield higher trajectory planning scores.
Yibo Yuan, Jiacheng Fu, Jiangtong Zhu et al.· 1 citation
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