TL;DR
This paper introduces a novel implicit multiplane image method for remote sensing view synthesis, enabling high-quality, flexible rendering of scenes from limited views using deep neural networks and a new dataset.
Contribution
The paper proposes the Implicit Multiplane Images (ImMPI) approach, combining implicit neural representations with multiplane images for improved remote sensing view synthesis.
Findings
Outperforms state-of-the-art in reconstruction accuracy and visual fidelity.
Enables efficient rendering from sparse multi-view inputs.
Provides a new dataset for remote sensing view synthesis.
Abstract
Novel view synthesis of remote sensing scenes is of great significance for scene visualization, human-computer interaction, and various downstream applications. Despite the recent advances in computer graphics and photogrammetry technology, generating novel views is still challenging particularly for remote sensing images due to its high complexity, view sparsity and limited view-perspective variations. In this paper, we propose a novel remote sensing view synthesis method by leveraging the recent advances in implicit neural representations. Considering the overhead and far depth imaging of remote sensing images, we represent the 3D space by combining implicit multiplane images (MPI) representation and deep neural networks. The 3D scene is reconstructed under a self-supervised optimization paradigm through a differentiable multiplane renderer with multi-view input constraints. Images…
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