mlsanityData to 7 Oct 2026

GeoScaler: Geometry and Rendering-Aware Downsampling of 3D Mesh Textures

arXiv 2311.16581 ↗First posted 28 Nov 2023cs.GR cs.CVcited by 3

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Abstract (arXiv)

High-resolution texture maps are necessary for representing real-world objects accurately with 3D meshes. The large sizes of textures can bottleneck the real-time rendering of high-quality virtual 3D scenes on devices having low computational budgets and limited memory. Downsampling the texture maps directly addresses the issue, albeit at the cost of visual fidelity. Traditionally, downsampling of texture maps is performed using methods like bicubic interpolation and the Lanczos algorithm. These methods ignore the geometric layout of the mesh and its UV parametrization and also do not account for the rendering process used to obtain the final visualization that the users will experience. Towards filling these gaps, we introduce GeoScaler, which is a method of downsampling texture maps of 3D meshes while incorporating geometric cues, and by maximizing the visual fidelity of the rendered views of the textured meshes. We show that the textures generated by GeoScaler deliver significantly better quality rendered images compared to those generated by traditional downsampling methods

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 26 of its 65 references that are in the map. First posted on arXiv 28 Nov 2023.

Builds on

26 of 65 references.

References · 26

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  4. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  5. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  6. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  7. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  8. 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 781
  9. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 649
  10. 2022Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Itemscited by 601
  11. 2017Neural 3D Mesh Renderercited by 437
  12. 2019Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoningcited by 420
14 more
  1. 2022MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architecturescited by 308
  2. 2022Unifying Flow, Stereo and Depth Estimationcited by 270
  3. 2022Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wildcited by 159
  4. 2022Recovering 3D Human Mesh from Monocular Images: A Surveycited by 103
  5. 2019DeepSFM: Structure From Motion Via Deep Bundle Adjustmentcited by 70
  6. 2019Inverse Path Tracing for Joint Material and Lighting Estimationcited by 62
  7. 2021Subdivision-Based Mesh Convolution Networkscited by 60
  8. 2020Primal-Dual Mesh Convolutional Neural Networkscited by 44
  9. 20193D-RelNet: Joint Object and Relational Network for 3D Predictioncited by 28
  10. 2021Differentiable Stereopsis: Meshes from multiple views using differentiable renderingcited by 26
  11. 2022Textured Mesh Quality Assessment: Large-Scale Dataset and Deep Learning-based Quality Metriccited by 26
  12. 2023Random-Access Neural Compression of Material Texturescited by 23
  13. 2022Learning 3D Object Shape and Layout without 3D Supervisioncited by 19
  14. 2022DA Wand: Distortion-Aware Selection using Neural Mesh Parameterizationcited by 9

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Citing papers per year: 2024: 1; 2025: 2.

3 papers.