Unstructured Multi-view Depth Estimation Using Mask-Based Multiplane Representation

Yuxin Hou*, Arno Solin, Juho Kannala

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

3 Citations (Scopus)


This paper presents a novel method, MaskMVS, to solve depth estimation for unstructured multi-view image-pose pairs. In the plane-sweep procedure, the depth planes are sampled by histogram matching that ensures covering the depth range of interest. Unlike other plane-sweep methods, we do not rely on a cost metric to explicitly build the cost volume, but instead infer a multiplane mask representation which regularizes the learning. Compared to many previous approaches, we show that our method is lightweight and generalizes well without requiring excessive training. We outperform the current state-of-the-art and show results on the sun3d, scenes11, MVS, and RGBD test data sets.

Original languageEnglish
Title of host publicationImage Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings
EditorsMichael Felsberg, Per-Erik Forssén, Jonas Unger, Ida-Maria Sintorn
Number of pages13
ISBN (Print)9783030202040
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Conference publication
EventScandinavian Conference on Image Analysis - Norrköping, Sweden
Duration: 11 Jun 201913 Jun 2019
Conference number: 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11482 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceScandinavian Conference on Image Analysis
Abbreviated titleSCIA


  • Computer vision
  • Depth estimation
  • Multi-view stereo


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