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Related Experiment Video

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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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Instant Automatic Emptying of Panoramic Indoor Scenes.

Giovanni Pintore, Marco Agus, Eva Almansa

IEEE Transactions on Visualization and Computer Graphics

|September 6, 2022

Summary

This study introduces a novel data-driven method for diminished reality (DR) in 360° images, automatically removing clutter to create photorealistic, empty scenes with depth maps efficiently.

Area of Science:

Computer Vision
Extended Reality (XR)
Diminished Reality (DR)

Background:

360° cameras are vital for Extended Reality (XR) applications.
Diminished Reality (DR) techniques are needed to conceal objects in XR.
Existing methods often focus on single object removal based on semantics.

Purpose of the Study:

To develop a data-driven approach for automatically removing clutter from 360° indoor scenes.
To generate photorealistic, clutter-free omnidirectional views with accurate depth maps.
To achieve low-latency processing for real-time applications.

Main Methods:

A holistic, data-driven approach applied to the entire 360° scene.
Calculation of an attention mask based on geometric differences between full and empty scenes.

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Gated convolutions for generating output images and depth, leveraging geometric losses during training.

Main Results:

The method achieves very low latency and outperforms state-of-the-art solutions in prediction accuracy.
It successfully separates clutter from architectural structure in a single step.
Consistent quality results are observed even for real-world scenes without ground truth data.

Conclusions:

The proposed method offers an efficient and accurate solution for clutter removal in 360° indoor environments.
It enables advanced Diminished Reality (DR) applications within Extended Reality (XR).
The lightweight network design ensures interactive performance for practical use.