MDP-Omni: Parameter-free Multimodal Depth Prior-based Sampling for Omnidirectional Stereo Matching
Eunjin Son, HyungGi Jo, Wookyong Kwon, Sang Jun Lee
Abstract
Omnidirectional stereo matching (OSM) estimates 360deg depth by performing stereo matching on multi-view fisheye images. Existing methods assume a unimodal depth distribution, matching each pixel to a single object. However, this assumption constrains the sampling range, causing over-smoothed depth artifacts, especially at object boundaries. To address these limitations, we propose MDP-Omni, a novel OSM network that leverages parameter-free multimodal depth priors. Specifically, we design a sampling strategy that adaptively adjusts the sampling range based on a multimodal probability distribution, without introducing any additional parameters. Furthermore, we present the azimuth-based multi-view volume fusion module to build a single cost volume. It mitigates false matches caused by occlusions in warped multi-view volumes. Experimental results demonstrate that MDP-Omni significantly improves existing methods, particularly in capturing fine details.
BibTeX
@InProceedings{Son_2025_ICCV,
author = {Son, Eunjin and Jo, HyungGi and Kwon, Wookyong and Lee, Sang Jun},
title = {MDP-Omni: Parameter-free Multimodal Depth Prior-based Sampling for Omnidirectional Stereo Matching},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26178-26187}
}