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Budgeted-GS enables real-time large-scale 3D Gaussian Splatting on consumer GPUs

By PulseAugur Editorial · [5 sources] ·

Researchers have developed Budgeted-GS, a novel post-hoc method designed to enable real-time rendering of large-scale 3D Gaussian Splatting models, such as city-scale environments, on consumer GPUs. This technique factors trained models into a multi-resolution hierarchy, allowing for dynamic level-of-detail selection based on available memory. Additionally, Budgeted-GS incorporates a budget-centered training approach that directly optimizes the number of primitives needed for a scene, avoiding wasted effort on redundant elements. The method is validated on various scenes, demonstrating real-time rendering capabilities at high quality on standard hardware. AI

IMPACT Enables real-time rendering of large-scale 3D scenes on consumer hardware, potentially impacting AR/VR and gaming.

RANK_REASON Research paper detailing a new method for 3D Gaussian Splatting. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

Budgeted-GS enables real-time large-scale 3D Gaussian Splatting on consumer GPUs

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Research paper detailing a new method for 3D Gaussian Splatting. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [5]

  1. arXiv cs.CV TIER_1 English(EN) · Minhyeok Lee, Jungho Lee, Minseok Kang, Heeseung Choi, Ig-Jae Kim, Sangyoun Lee ·

    DensiTok: Making Feed-Forward 3D Gaussian Splatting See More Views Than It Is Given

    arXiv:2610.07958v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) reconstructs a scene in a single forward pass, replacing per-scene optimization with a network trained across many scenes. Its quality, however, degrades sharply as the number of input image…

  2. arXiv cs.CV TIER_1 English(EN) · Iv\'an Verdugo Guerra, Ezequiel L\'opez Rubio, Jorge Garc\'ia Gonz\'alez ·

    Post-Training Semantic Lifting for 3D Gaussian Splatting: Separating Detector, Lifting and Representation Error

    arXiv:2610.08756v1 Announce Type: new Abstract: The same Gaussian of a 3D Gaussian Splatting model is seen from many views, and these views do not always agree on the class it belongs to. The Gaussian may be occluded in some of them, and the confidence of the detector is not the …

  3. arXiv cs.CV TIER_1 English(EN) · Tsuheng Hsu, Guiyu Liu, Juho Kannala, Janne Heikkil\"a ·

    Scene-Agnostic Object-Centric Representation Learning for 3D Gaussian Splatting

    arXiv:2604.09045v2 Announce Type: replace Abstract: Recent works on 3D scene understanding leverage 2D masks from visual foundation models (VFMs) to supervise radiance fields, enabling instance-level 3D segmentation. However, the supervision signals from foundation models are not…

  4. arXiv cs.CV TIER_1 English(EN) · Junyeong Ahn, Jaegul Choo ·

    VolS-GS: Relightable Gaussian Splatting with Volumetric Subsurface Scattering

    arXiv:2610.04007v2 Announce Type: replace Abstract: We present VolS-GS, a relightable Gaussian splatting framework that reconstructs objects from one-light-at-a-time (OLAT) captures and renders them under novel lighting and viewpoints. Relightable Gaussian Splatting methods typic…

  5. arXiv cs.CV TIER_1 English(EN) · Haipeng Wang ·

    Budgeted-GS: Real-Time Large-Scale Gaussian Splatting via Factoring LOD

    arXiv:2610.03162v1 Announce Type: cross Abstract: 3D Gaussian Splatting achieves excellent visual quality with real-time rendering, but at the scale of entire cities it does not fit: a trained model carries millions of primitives and gigabytes of memory, and real-time rendering a…

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