[2601.03824] IDESplat: Iterative Depth Probability Estimation for Generalizable 3D Gaussian Splatting

Computer Science > Computer Vision and Pattern Recognition arXiv:2601.03824 (cs) [Submitted on 7 Jan 2026 (v1), last revised 26 Mar 2026 (this version, v3)] Title:IDESplat: Iterative Depth Probability Estimation for Generalizable 3D Gaussian Splatting Authors:Wei Long, Haifeng Wu, Shiyin Jiang, Jinhua Zhang, Xinchun Ji, Shuhang Gu View a PDF of the paper titled IDESplat: Iterative Depth Probability Estimation for Generalizable 3D Gaussian Splatting, by Wei Long and 5 other authors View PDF HTML (experimental) Abstract:Generalizable 3D Gaussian Splatting aims to directly predict Gaussian parameters using a feed-forward network for scene reconstruction. Among these parameters, Gaussian means are particularly difficult to predict, so depth is usually estimated first and then unprojected to obtain the Gaussian sphere centers. Existing methods typically rely solely on a single warp to estimate depth probability, which hinders their ability to fully leverage cross-view geometric cues, resulting in unstable and coarse depth maps. To address this limitation, we propose IDESplat, which iteratively applies warp operations to boost depth probability estimation for accurate Gaussian mean prediction. First, to eliminate the inherent instability of a single warp, we introduce a Depth Probability Boosting Unit (DPBU) that integrates epipolar attention maps produced by cascading warp operations in a multiplicative manner. Next, we construct an iterative depth estimation process by stackin...