DENSER: 3D Gaussian Splatting for Scene Reconstruction of Dynamic Urban Environments

Mahmud A. Mohamad, Gamal Elghazaly, Arthur Hubert, Raphaël Frank

Abstract: This paper presents DENSER, a framework leveraging 3D Gaussian splatting (3DGS) for the reconstruction of dynamic urban environments. While several methods for photorealistic scene representations, both implicitly using neural radiance fields (NeRF) and explicitly using 3DGS have shown promising results in scene reconstruction of relatively complex dynamic scenes, modeling the dynamic appearance of foreground objects tends to be challenging, limiting the applicability of these methods to capture subtleties and details of the scenes, especially for dynamic objects. To this end, we propose DENSER, a framework that significantly enhances the representation of dynamic objects and accurately models the appearance of dynamic objects in the driving scene. Instead of directly using Spherical Harmonics (SH) to model the appearance of dynamic objects, we introduce and integrate a new method aiming at dynamically estimating SH bases using wavelets, resulting in better representation of dynamic objects appearance in both space and time. Besides object appearance, DENSER enhances object shape representation through densification of its point cloud across multiple scene frames, resulting in faster convergence of model training. Extensive evaluations on the KITTI dataset show that the proposed approach outperforms state-of-the-art methods by a wide margin. Source codes and models will be uploaded to this repository https://github.com/sntubix/denser

External IDs:dblp:conf/icra/MohamadEHF25