metadata
license: mit
pipeline_tag: depth-estimation
IDESplat: Iterative Depth Probability Estimation for Generalizable 3D Gaussian Splatting
Wei Long 路 Haifeng Wu 路 Shiyin Jiang 路 Jinhua Zhang 路 Xinchun Ji 路 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.