Diversity-Driven View Subset Selection for Indoor Novel View Synthesis

TL;DR

This paper introduces a novel subset selection framework based on diversity measurement for indoor scene view synthesis, significantly reducing data requirements while maintaining high-quality results.

Contribution

It presents a new diversity-driven subset selection method with theoretical analysis and validates it on a new indoor dataset, IndoorTraj, outperforming baseline strategies.

Findings

Achieves high-quality view synthesis using only 5-20% of the data.

Outperforms baseline strategies in efficiency and effectiveness.

Introduces the IndoorTraj dataset for indoor view synthesis research.

Abstract

Novel view synthesis of indoor scenes can be achieved by capturing a monocular video sequence of the environment. However, redundant information caused by artificial movements in the input video data reduces the efficiency of scene modeling. To address this, we formulate the problem as a combinatorial optimization task for view subset selection. In this work, we propose a novel subset selection framework that integrates a comprehensive diversity-based measurement with well-designed utility functions. We provide a theoretical analysis of these utility functions and validate their effectiveness through extensive experiments. Furthermore, we introduce IndoorTraj, a novel dataset designed for indoor novel view synthesis, featuring complex and extended trajectories that simulate intricate human behaviors. Experiments on IndoorTraj show that our framework consistently outperforms baseline strategies while using only 5-20% of the data, highlighting its remarkable efficiency and effectiveness. The code is available at: https://github.com/zehao-wang/IndoorTraj