Pith. sign in

REVIEW 2 cited by

GenPluSSS: A Genetic Algorithm Based Plugin for Measured Subsurface Scattering Representation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2401.15245 v1 pith:QBTS3EW4 submitted 2024-01-26 cs.GR cs.AIcs.LGcs.NE

Bar{i}ş Y{i}ld{i}r{i}m , Murat Kurt This is my paper · ORCID
classification cs.GRcs.AIcs.LGcs.NE
keywords pluginscatteringsubsurfaceproposedalgorithmbeengeneticheterogeneous
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper presents a plugin that adds a representation of homogeneous and heterogeneous, optically thick, translucent materials on the Blender 3D modeling tool. The working principle of this plugin is based on a combination of Genetic Algorithm (GA) and Singular Value Decomposition (SVD)-based subsurface scattering method (GenSSS). The proposed plugin has been implemented using Mitsuba renderer, which is an open source rendering software. The proposed plugin has been validated on measured subsurface scattering data. It's shown that the proposed plugin visualizes homogeneous and heterogeneous subsurface scattering effects, accurately, compactly and computationally efficiently.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LightFFDNets: Lightweight Convolutional Neural Networks for Rapid Facial Forgery Detection

    cs.CV 2024-11 conditional novelty 3.0 of 10

    Two minimal convolutional networks match big pretrained models on an easy fake-face dataset and train far faster, but they fail on a harder 140k face dataset.

  2. Mul2MAR: A Multi-Marker Mobile Augmented Reality Application for Improved Visual Perception

    cs.GR 2025-02 reject novelty 2.0 of 10

    Mul2MAR combines ARToolKit markers, OpenGL rendering, and red-cyan anaglyph glasses to show virtual objects in apparent 3D on a mobile device, but gives no quantitative validation beyond the author's prior work.

Pith tools