Title: DDI-Gaussian: Distributed Dynamic Instance Gaussian for Autonomous Driving
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: 3D Gaussian、Autonomous Driving、Instance-Level、Dynamic Scene Reconstruction

Abstract: Current dynamic 3D Gaussian approaches are still limited to surface and appearance modeling and lack fine-grained, instance-level scene understanding. Such capability is not only essential for downstream applications but also facilitates scene reconstruction, such as capturing transient objects, modeling dynamic appearance changes, and decomposing scenes and motions. To bridge this gap, we propose Distributed Dynamic Instance Gaussians (DDIG), a novel framework for instance-aware reconstruction in urban street scenes. Our key innovation lies in assigning each Gaussian a compact multi-hot instance feature, enabling direct differentiation without relying on additional network. To model transient motions, we initialize sparse control points at the instance level and construct the motion field from coarse to fine by leveraging temporal and spatial positional relationships. Additionally, we introduce two novel loss functions: an instance-level region loss and an instance-semantic loss. The instance-level region loss, combined with the opacity rendering pipeline, enables precise instance-level rendering and suppresses ghosting artifacts. The instance-semantic loss enforces cross-view feature consistency. The entire framework operates in a distributed manner. Notably, our framework avoids costly 3D instance annotations by instead utilizing 2D pseudo-labels generated by Segment Anything Model (SAM) for supervision. Extensive experiments on both public datasets and real-world collected data demonstrate that our approach achieves state-of-the-art rendering quality and accurate instance-level understanding. Our demo video and code are available in the anonymous repository at  https://anonymous.4open.science/r/DDI-GS-750E/.

TL;DR: The proposed approach enables efficient discrimination of 3D Gaussians for instance-level scene understanding without requiring extra 3D annotations or additional networks, and achieves state-of-the-art performance in dynamic scene reconstruction.