Understanding Dynamic Scenes in Ego Centric 4D Point Clouds

• URL: http://arxiv.org/abs/2508.07251v2
• Date: Tue, 12 Aug 2025 15:01:27 GMT
• Title: Understanding Dynamic Scenes in Ego Centric 4D Point Clouds
• Authors: Junsheng Huang, Shengyu Hao, Bocheng Hu, Gaoang Wang,
• Abstract summary: EgoDynamic4D is a novel QA benchmark on highly dynamic scenes.<n>We design 12 dynamic QA tasks covering agent motion, human-object interaction prediction, relation, understanding trajectory, temporal-causal reasoning, with fine-grained, explicit metrics.<n>Our method consistently outperforms baselines, validating the effectiveness of multimodal temporal modeling for ego-centric dynamic scene understanding.
• Score: 7.004204907286336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding dynamic 4D scenes from an egocentric perspective-modeling changes in 3D spatial structure over time-is crucial for human-machine interaction, autonomous navigation, and embodied intelligence. While existing egocentric datasets contain dynamic scenes, they lack unified 4D annotations and task-driven evaluation protocols for fine-grained spatio-temporal reasoning, especially on motion of objects and human, together with their interactions. To address this gap, we introduce EgoDynamic4D, a novel QA benchmark on highly dynamic scenes, comprising RGB-D video, camera poses, globally unique instance masks, and 4D bounding boxes. We construct 927K QA pairs accompanied by explicit Chain-of-Thought (CoT), enabling verifiable, step-by-step spatio-temporal reasoning. We design 12 dynamic QA tasks covering agent motion, human-object interaction, trajectory prediction, relation understanding, and temporal-causal reasoning, with fine-grained, multidimensional metrics. To tackle these tasks, we propose an end-to-end spatio-temporal reasoning framework that unifies dynamic and static scene information, using instance-aware feature encoding, time and camera encoding, and spatially adaptive down-sampling to compress large 4D scenes into token sequences manageable by LLMs. Experiments on EgoDynamic4D show that our method consistently outperforms baselines, validating the effectiveness of multimodal temporal modeling for egocentric dynamic scene understanding.

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