NOVA: 自動運転における3Dマルチオブジェクト追跡のための次段階オープンボキャブラリ自己回帰
NOVA: Next-step Open-Vocabulary Autoregression for 3D Multi-Object Tracking in Autonomous Driving
3Dマルチオブジェクト追跡を、大規模言語モデルの自己回帰能力を利用して、軌跡を時空間セマンティックシーケンスとして扱い、次段階のシーケンス補完として定式化する新しい手法を提案。未知の物体カテゴリへの一般化を向上させ、nuScenesなどのデータセットで優れた性能を達成。
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著者: Kai Luo, Xu Wang, Rui Fan, Kailun Yang
分類: cs.CV, cs.RO, eess.IV
原文アブストラクト
Generalizing across unknown targets is critical for open-world perception, yet existing 3D Multi-Object Tracking (3D MOT) pipelines remain limited by closed-set assumptions and ``semantic-blind'' heuristics. To address this, we propose Next-step Open-Vocabulary Autoregression (NOVA), an autoregressive association formulation that shifts the data association stage from fragmented distance-based matching toward trajectory-conditioned spatio-semantic modeling. NOVA reformulates 3D trajectories as structured spatio-temporal semantic sequences, enabling the simultaneous encoding of physical motion continuity and deep linguistic priors. By leveraging the autoregressive capabilities of Large Language Models (LLMs), we transform the tracking task into a principled process of next-step sequence completion. This mechanism allows the model to explicitly utilize the hierarchical structure of language space to resolve fine-grained semantic ambiguities and maintain identity consistency across complex long-range sequences through high-level commonsense reasoning. Extensive experiments on nuScenes, V2X-Seq-SPD, and KITTI demonstrate the superior performance of NOVA. Notably, on the nuScenes dataset, NOVA achieves an AMOTA of 22.41% for Novel categories, yielding a significant 20.21% absolute improvement over the baseline. These gains are realized through a compact 0.5B autoregressive model. Code will be available at https://github.com/xifen523/NOVA.
関連論文
- 自動運転車における前方動的物体の3D追跡フレームワーク3D追跡/自動運転
