Depth-of-Field Segmentation for Near-lossless Image Compression and 3D Reconstruction

Buelow, Max von; Tausch, Reimar; Schurig, Martin; Knauthe, Volker; Wirth, Tristan; Guthe, Stefan; Santos, Pedro; Fellner, Dieter W.

doi:10.1145/3500924

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…The unsharp regions of the rectified images are masked out based on the depth of field (DOF), a volume projected onto the captured plane [17]. The DOF volume is a slice of the pyramidal FOV volume that lies between the near and the far planes.…”

Section: Depth Of Field Masking and Vignettingmentioning

confidence: 99%

Color Calibration based on Mosaic Stitching of a Color Target as an Alternative to a Single-shot Approach

Abu Haila,

Schneider,

Tausch

et al. 2023

archiving

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Color targets come in different designs, sizes and surface finishes. A high quality color target such as the Next Generation Target (NGT) 1 , designed for the Library of Congress, has a glossy finish that makes it sensitive to the light-setup geometry. When the NGT color target is to be captured orthogonally, i.e. both the camera and the light share the same plane and lie on the normal of the target's surface, even with cross-polarization in place it is not possible to completely eliminate the high reflections caused by the camera/light geometry -unlike for less glossy color targets such as the X-Rite SG CC-not even if the camera/light setup were to be tilted at different angles. We are demonstrating in this paper that it is possible, however, to deploy a mosaic approach to capture the NGT color target at a tilted angle, masking out the reflections, and composing a rectified mosaic image out of only the clear parts of the target. The resultant ICC color correction profile for the mosaic image is proved to be viable to put in use and it satisfies all the necessary metrics for ISO level "A" when it comes to color calibration and color accuracy.

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Section: Depth Of Field Masking and Vignettingmentioning

confidence: 99%

Color Calibration based on Mosaic Stitching of a Color Target as an Alternative to a Single-shot Approach

Abu Haila,

Schneider,

Tausch

et al. 2023

archiving

View full text Add to dashboard Cite

show abstract

NeRF-FF: a plug-in method to mitigate defocus blur for runtime optimized neural radiance fields

Wirth,

Rak,

von Buelow

et al. 2024

Vis Comput

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Neural radiance fields (NeRFs) have revolutionized novel view synthesis, leading to an unprecedented level of realism in rendered images. However, the reconstruction quality of NeRFs suffers significantly from out-of-focus regions in the input images. We propose NeRF-FF, a plug-in strategy that estimates image masks based on Focus Frustums (FFs), i.e., the visible volume in the scene space that is in-focus. NeRF-FF enables a subsequently trained NeRF model to omit out-of-focus image regions during the training process. Existing methods to mitigate the effects of defocus blurred input images often leverage dynamic ray generation. This makes them incompatible with the static ray assumptions employed by runtime-performance-optimized NeRF variants, such as Instant-NGP, leading to high training times. Our experiments show that NeRF-FF outperforms state-of-the-art approaches regarding training time by two orders of magnitude—reducing it to under 1 min on end-consumer hardware—while maintaining comparable visual quality.

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Depth-of-Field Segmentation for Near-lossless Image Compression and 3D Reconstruction

Cited by 2 publications

References 18 publications

Color Calibration based on Mosaic Stitching of a Color Target as an Alternative to a Single-shot Approach

Color Calibration based on Mosaic Stitching of a Color Target as an Alternative to a Single-shot Approach

NeRF-FF: a plug-in method to mitigate defocus blur for runtime optimized neural radiance fields

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