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PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy
Abstract Endoluminal endoscopic imaging—used in diagnosing colorectal cancer and other internal diseases—stands to benefit greatly from accurate 3D reconstructions and novel-view synthesis. However, current methods struggle with artifacts due to constrained camera trajectories and lighting effects that depend heavily on view, often overfitting and failing when viewing from novel angles.
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MiraGe: Editable 2.5D Image Representations with Flat-Controlled 3D Gaussians
Abstract Implicit Neural Representations (INRs) encode images as continuous functions that map pixel coordinates to RGB values, achieving compact storage and high visual fidelity. Recent work such as GaussianImage replaces neural MLPs with collections of 2D Gaussian primitives, reaching similar reconstruction quality and compression but offering limited editability. In practice, creators often need to adjust content—move objects, bend a photo, cast new shadows, or create parallax—all of which are awkward within purely 2D or purely additive Gaussian schemes.
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NegGS: Negative Gaussian Splatting
Abstract One of the key strengths of 3D rendering lies in its ability to simulate intricate scenes with high fidelity. Among the recent advancements, Gaussian Splatting has gained prominence for its fast training and inference capabilities.
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