Prioritized Multi-View Stereo Depth Map Generation Using Confidence Prediction

TL;DR

This paper introduces a machine learning-based prioritization method for multi-view stereo depth map generation, significantly reducing computational costs while maintaining high-quality 3D reconstructions across diverse scenarios.

Contribution

It presents a novel confidence prediction technique that does not require ground truth data, enabling effective view prioritization for efficient 3D reconstruction.

Findings

Achieves over 70% of maximum quality with only 5% of images as key views.

Works effectively across cultural heritage and architectural reconstruction scenarios.

Uses a confidence predictor trained without manual labels for improved view selection.

Abstract

In this work, we propose a novel approach to prioritize the depth map computation of multi-view stereo (MVS) to obtain compact 3D point clouds of high quality and completeness at low computational cost. Our prioritization approach operates before the MVS algorithm is executed and consists of two steps. In the first step, we aim to find a good set of matching partners for each view. In the second step, we rank the resulting view clusters (i.e. key views with matching partners) according to their impact on the fulfillment of desired quality parameters such as completeness, ground resolution and accuracy. Additional to geometric analysis, we use a novel machine learning technique for training a confidence predictor. The purpose of this confidence predictor is to estimate the chances of a successful depth reconstruction for each pixel in each image for one specific MVS algorithm based on the RGB images and the image constellation. The underlying machine learning technique does not require any ground truth or manually labeled data for training, but instead adapts ideas from depth map fusion for providing a supervision signal. The trained confidence predictor allows us to evaluate the quality of image constellations and their potential impact to the resulting 3D reconstruction and thus builds a solid foundation for our prioritization approach. In our experiments, we are thus able to reach more than 70% of the maximal reachable quality fulfillment using only 5% of the available images as key views. For evaluating our approach within and across different domains, we use two completely different scenarios, i.e. cultural heritage preservation and reconstruction of single family houses.