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

Wei Long · Haifeng Wu · Shiyin Jiang · Jinhua Zhang · Xinchun Ji · Shuhang Gu

CVPR 2026

Paper | Code

Architecture

The overall architecture of IDESplat. IDESplat is composed of three key parts: a feature extraction backbone, an iterative depth probability estimation process, and a Gaussian Focused Module (GFM). The iterative process consists of cascaded Depth Probability Boosting Units (DPBUs). Each unit combines multi-level warp results in a multiplicative manner to mitigate the inherent instability of a single warp. As IDESplat iteratively updates the depth candidates and boosts the probability estimates, the depth map becomes more precise, leading to accurate Gaussian means.