Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene

• URL: http://arxiv.org/abs/2503.15019v1
• Date: Wed, 19 Mar 2025 09:16:08 GMT
• Title: Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene
• Authors: Shengqiong Wu, Hao Fei, Jingkang Yang, Xiangtai Li, Juncheng Li, Hanwang Zhang, Tat-seng Chua,
• Abstract summary: This paper investigates a novel framework for 4D-PSG generation that leverages rich 2D visual scene annotations to enhance 4D scene learning.<n>We propose a 2D-to-4D visual scene transfer learning framework, where a spatial-temporal scene strategy effectively transfers dimension-invariant features from abundant 2D SG annotations to 4D scenes.
• Score: 122.42861221739123
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The latest emerged 4D Panoptic Scene Graph (4D-PSG) provides an advanced-ever representation for comprehensively modeling the dynamic 4D visual real world. Unfortunately, current pioneering 4D-PSG research can primarily suffer from data scarcity issues severely, as well as the resulting out-of-vocabulary problems; also, the pipeline nature of the benchmark generation method can lead to suboptimal performance. To address these challenges, this paper investigates a novel framework for 4D-PSG generation that leverages rich 2D visual scene annotations to enhance 4D scene learning. First, we introduce a 4D Large Language Model (4D-LLM) integrated with a 3D mask decoder for end-to-end generation of 4D-PSG. A chained SG inference mechanism is further designed to exploit LLMs' open-vocabulary capabilities to infer accurate and comprehensive object and relation labels iteratively. Most importantly, we propose a 2D-to-4D visual scene transfer learning framework, where a spatial-temporal scene transcending strategy effectively transfers dimension-invariant features from abundant 2D SG annotations to 4D scenes, effectively compensating for data scarcity in 4D-PSG. Extensive experiments on the benchmark data demonstrate that we strikingly outperform baseline models by a large margin, highlighting the effectiveness of our method.

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