README.md

---
license: mit
pipeline_tag: depth-estimation
---

<p align="center">
  <h1 align="center">IDESplat: Iterative Depth Probability Estimation for Generalizable 3D Gaussian Splatting</h1>
  <p align="center">
    <a href="https://scholar.google.com/citations?user=CsVTBJoAAAAJ&hl=zh-CN">Wei Long</a>
    ·
    <a href="https://scholar.google.com/citations?user=rvVphXoAAAAJ&hl=zh-CN&oi=ao">Haifeng Wu</a>
    ·
    <a href="https://openreview.net/profile?id=~Shiyin_Jiang1">Shiyin Jiang</a>
    ·
    <a href="https://scholar.google.com/citations?user=tyYxiXoAAAAJ&hl=zh-CN">Jinhua Zhang</a>
    ·
    <a href="https://openreview.net/profile?id=~Xinchun_Ji2">Xinchun Ji</a>
    ·
    <a href="https://scholar.google.com/citations?user=-kSTt40AAAAJ&hl=zh-CN">Shuhang Gu</a>
  </p>
  <h3 align="center">CVPR 2026</h3>
  <h3 align="center"><a href="https://arxiv.org/abs/2601.03824">Paper</a> | <a href="https://github.com/CVL-UESTC/IDESplat">Code</a></h3>
  <div align="center"></div>
</p>

<p align="center">
  <a href="">
    <img src="figures/IDESplat_m.png" alt="IDESplat Overview" width="100%">
  </a>
</p>

## Architecture

<p align="center">
  <a href="">
    <img src="figures/IDESplat_net.png" alt="IDESplat Overview" width="100%">
  </a>
</p>

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.