Descriptor-Free Multi-View Region Matching for Instance-Wise 3D Reconstruction

TL;DR

This paper introduces a descriptor-free multi-view region matching method based on epipolar geometry, enhancing instance-wise 3D reconstruction accuracy without relying on texture or shape descriptors.

Contribution

It presents a novel multi-view region matching approach that integrates epipolar geometry into instance segmentation for improved 3D reconstruction.

Findings

Enhanced accuracy of multi-view instance matching

Improved 3D reconstruction results over baseline methods

Effective integration of epipolar region matching into segmentation

Abstract

This paper proposes a multi-view extension of instance segmentation without relying on texture or shape descriptor matching. Multi-view instance segmentation becomes challenging for scenes with repetitive textures and shapes, e.g., plant leaves, due to the difficulty of multi-view matching using texture or shape descriptors. To this end, we propose a multi-view region matching method based on epipolar geometry, which does not rely on any feature descriptors. We further show that the epipolar region matching can be easily integrated into instance segmentation and effective for instance-wise 3D reconstruction. Experiments demonstrate the improved accuracy of multi-view instance matching and the 3D reconstruction compared to the baseline methods.