DIAL-GS: Dynamic Instance Aware Reconstruction for Label-free Street Scenes with 4D Gaussian Splatting

• URL: http://arxiv.org/abs/2511.06632v1
• Date: Mon, 10 Nov 2025 02:18:40 GMT
• Title: DIAL-GS: Dynamic Instance Aware Reconstruction for Label-free Street Scenes with 4D Gaussian Splatting
• Authors: Chenpeng Su, Wenhua Wu, Chensheng Peng, Tianchen Deng, Zhe Liu, Hesheng Wang,
• Abstract summary: We propose DIAL-GS, a novel dynamic instance-aware reconstruction method for label-free street scenes.<n>We first accurately identify dynamic instances by exploiting appearance-position inconsistency between warped rendering and actual observation.<n>We introduce a reciprocal mechanism through which identity and dynamics reinforce each other, enhancing both integrity and consistency.
• Score: 23.017838856573917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urban scene reconstruction is critical for autonomous driving, enabling structured 3D representations for data synthesis and closed-loop testing. Supervised approaches rely on costly human annotations and lack scalability, while current self-supervised methods often confuse static and dynamic elements and fail to distinguish individual dynamic objects, limiting fine-grained editing. We propose DIAL-GS, a novel dynamic instance-aware reconstruction method for label-free street scenes with 4D Gaussian Splatting. We first accurately identify dynamic instances by exploiting appearance-position inconsistency between warped rendering and actual observation. Guided by instance-level dynamic perception, we employ instance-aware 4D Gaussians as the unified volumetric representation, realizing dynamic-adaptive and instance-aware reconstruction. Furthermore, we introduce a reciprocal mechanism through which identity and dynamics reinforce each other, enhancing both integrity and consistency. Experiments on urban driving scenarios show that DIAL-GS surpasses existing self-supervised baselines in reconstruction quality and instance-level editing, offering a concise yet powerful solution for urban scene modeling.

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