Eve3D: Elevating Vision Models for Enhanced 3D Surface Reconstruction via Gaussian Splatting
Jiawei Zhang, Youmin Zhang, Fabio Tosi, Meiying Gu, Jiahe Li, Xiaohan Yu, Jin Zheng, Xiao Bai, Matteo Poggi
We present Eve3D, a novel framework for dense 3D reconstruction based on 3D Gaussian Splatting (3DGS). While most existing methods rely on imperfect priors derived from pre-trained vision models, Eve3D fully leverages these priors by jointly optimizing both them and the 3DGS backbone. This joint optimization creates a mutually reinforcing cycle: the priors enhance the quality of the 3DGS, which in turn refines the priors, further improving the reconstruction. Additionally, Eve3D introduces a novel optimization step based on bundle adjustment, overcoming the limitations of the highly local supervision typical in standard 3DGS pipelines.Eve3D achieves state-of-the-art results in surface reconstruction and novel view synthesis on the Tanks \& Temples, DTU, and Mip-NeRF360 datasets. while retaining fast convergence, highlighting an unprecedented trade-off between accuracy and speed.