PoI: Pixel of Interest for Novel View Synthesis Assisted Scene Coordinate Regression
Keywords: visual localization, scene coordinate regresion, novel view synthesis
Abstract: The task of estimating camera poses can be enhanced through novel view synthesis techniques such as NeRF and Gaussian Splatting to increase the diversity and extension of training data. However, these techniques often produce rendered images with issues like blurring and ghosting, which compromise their reliability. These issues become particularly pronounced for Scene Coordinate Regression (SCR) methods, which estimate 3D coordinates at the pixel level. To mitigate the problems associated with unreliable rendered images, we introduce a novel filtering approach, which selectively extracts well-rendered pixels while discarding the inferior ones. The threshold of this filter is adaptively determined by the real-time reprojection loss recorded by the SCR models during training. Building on this filtering technique, we also develop a new strategy to improve scene coordinate regression using sparse inputs, drawing on successful applications of sparse input techniques in novel view synthesis. Our experimental results validate the effectiveness of our method, demonstrating the state-of-the-art performance on both indoor and outdoor datasets.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 3035
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