Implicit neural representation of textures
Not on the map. Why?
Abstract (arXiv)
Implicit neural representation (INR) has proven to be accurate and efficient in various domains. In this work, we explore how different neural networks can be designed as a new texture INR, which operates in a continuous manner rather than a discrete one over the input UV coordinate space. Through thorough experiments, we demonstrate that these INRs perform well in terms of image quality, with considerable memory usage and rendering inference time. We analyze the balance between these objectives. In addition, we investigate various related applications in real-time rendering and down-stream tasks, e.g. mipmap fitting and INR-space generation.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 2 Feb 2026.
Builds on
11 of 20 references.
References · 11
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
- 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
- 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
- 2013Describing Textures in the Wildcited by 2,125
- 2021Implicit Neural Representations for Image Compressioncited by 162
- 2023HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusioncited by 152
- 2025CHOrD: Generation of Collision-Free, House-Scale, and Organized Digital Twins for 3D Indoor Scenes with Controllable Floor Plans and Optimal Layoutscited by 10
- 2023VMAF Re-implementation on PyTorch: Some Experimental Resultscited by 4
- 2024M^3ashy: Multi-Modal Material Synthesis via Hyperdiffusioncited by 3
- 2016Image Quality Assessment for Performance Evaluation of Focus Measure Operatorscited by 2
Built on by
Not cited yet.