Splats under Pressure: Exploring Performance-Energy Trade-offs in Real-Time 3D Gaussian Splatting under Constrained GPU Budgets

• URL: http://arxiv.org/abs/2604.07177v1
• Date: Wed, 08 Apr 2026 15:05:29 GMT
• Title: Splats under Pressure: Exploring Performance-Energy Trade-offs in Real-Time 3D Gaussian Splatting under Constrained GPU Budgets
• Abstract summary: We investigate the feasibility of real-time 3D Gaussian Splatting (3DGS) on edge clients with varying GPU computational budgets.<n>Instead of evaluating multiple physical devices, we adopt an emulation-based approach.<n>We measure frame rate, runtime behaviour, and power consumption across scenes of varying complexity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We investigate the feasibility of real-time 3D Gaussian Splatting (3DGS) rasterisation on edge clients with varying Gaussian splat counts and GPU computational budgets. Instead of evaluating multiple physical devices, we adopt an emulation-based approach that approximates different GPU capability tiers on a single high-end GPU. By systematically under-clocking the GPU core frequency and applying power caps, we emulate a controlled range of floating-point performance levels that approximate different GPU capability tiers. At each point in this range, we measure frame rate, runtime behaviour, and power consumption across scenes of varying complexity, pipelines, and optimisations, enabling analysis of power-performance relationships such as FPS-power curves, energy per frame, and performance per watt. This method allows us to approximate the performance envelope of a diverse class of GPUs, from embedded and mobile-class devices to high-end consumer-grade systems. Our objective is to explore the practical lower bounds of client-side 3DGS rasterisation and assess its potential for deployment in energy-constrained environments, including standalone headsets and thin clients. Through this analysis, we provide early insights into the performance-energy trade-offs that govern the viability of edge-deployed 3DGS systems.

Related papers

• Local-GS: Accelerating 3D Gaussian Splatting via Tile-Local Warp Coherence [31.001161060131622]
  3D Gaussian Splatting (3DGS) has significantly advanced real-time novel view computation by representing scenes as dense collections of anisotropic 3D Gaussian primitives.<n>We present Local-GS, a warp-coherent rendering paradigm that organizes Gaussian primitives with respect to SIMT (Single Instruction, Multiple Threads) execution boundaries rather than scene geometry.<n>As a plug-and-play optimization, it provides additional performance gains to all tested baselines, culminating in a $7.76times$ speedup on Deep Blending scenes.
  arXiv  Detail & Related papers  (2026-06-15T11:08:10Z)
• X-GS: An Extensible Open Framework for Perceiving and Thinking via 3D Gaussian Splatting [72.02343855552051]
  We introduce X-GS, an open framework consisting of two major components: the X-GS-Perceiver and the X-GS-Thinker.<n>The Perceiver unifies a broad range of 3DGS techniques to enable real-time online SLAM and distill semantic features.<n>The Thinker accommodates vision-sampling models and utilizes the resulting 3D semantic Gaussians, enabling downstream applications such as object detection, caption generation, and potentially embodied tasks.
  arXiv  Detail & Related papers  (2026-03-10T13:10:18Z)
• GPU-Accelerated Algorithms for Graph Vector Search: Taxonomy, Empirical Study, and Research Directions [54.570944939061555]
  We present a comprehensive study of GPU-accelerated graph-based vector search algorithms.<n>We establish a detailed taxonomy of GPU optimization strategies and clarify the mapping between algorithmic tasks and hardware execution units.<n>Our findings offer clear guidelines for designing scalable and robust GPU-powered approximate nearest neighbor search systems.
  arXiv  Detail & Related papers  (2026-02-10T16:18:04Z)
• FlipFlop: A Static Analysis-based Energy Optimization Framework for GPU Kernels [38.75222180281849]
  FlipFlop is a framework using static code analysis to predict energy consumption and recommend optimal thread block configurations.<n>It achieves 83% accuracy in identifying optimal energy-efficient configurations, while also minimizing developer effort by reducing the optimization search space by 93.4%.<n>For multi-head attention kernels, it yields up to 79% energy savings and 106% throughput gains relative to NVIDIA's occupancy.
  arXiv  Detail & Related papers  (2026-01-19T19:30:25Z)
• Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspective [6.51239603014107]
  Large Language Models (LLMs) have pushed training workloads beyond the limits of single-node analysis.<n>We present a comprehensive characterization of LLM training across diverse real-world workloads and hardware platforms.
  arXiv  Detail & Related papers  (2025-09-12T16:05:07Z)
• FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting [57.97160965244424]
  3D Gaussian splatting (3DGS) has enabled various applications in 3D scene representation and novel view synthesis.<n>Previous approaches have focused on pruning less important Gaussians, effectively compressing 3DGS.<n>We present an elastic inference method for 3DGS, achieving substantial rendering performance without additional fine-tuning.
  arXiv  Detail & Related papers  (2025-06-04T17:17:57Z)
• LODGE: Level-of-Detail Large-Scale Gaussian Splatting with Efficient Rendering [75.67501939005119]
  We present a novel level-of-detail (LOD) method for 3D Gaussian Splatting on memory-constrained devices.<n>Our approach iteratively selects optimal subsets of Gaussians based on camera distance.<n>Our method achieves state-of-the-art performance on both outdoor (Hierarchical 3DGS) and indoor (Zip-NeRF) datasets.
  arXiv  Detail & Related papers  (2025-05-29T06:50:57Z)
• Steepest Descent Density Control for Compact 3D Gaussian Splatting [72.54055499344052]
  3D Gaussian Splatting (3DGS) has emerged as a powerful real-time, high-resolution novel view.<n>We propose a theoretical framework that demystifies and improves density control in 3DGS.<n>We introduce SteepGS, incorporating steepest density control, a principled strategy that minimizes loss while maintaining a compact point cloud.
  arXiv  Detail & Related papers  (2025-05-08T18:41:38Z)
• GauRast: Enhancing GPU Triangle Rasterizers to Accelerate 3D Gaussian Splatting [3.275890592583965]
  3D Gaussian Splatting (3DGS) is an emerging high-quality 3D rendering method.<n>Previous efforts to accelerate 3DGS rely on dedicated accelerators that require substantial integration overhead and hardware costs.<n>This work proposes an acceleration strategy that leverages the similarities between the 3DGS pipeline and the highly optimized conventional graphics pipeline.
  arXiv  Detail & Related papers  (2025-03-20T19:54:05Z)
• Multi-GPU RI-HF Energies and Analytic Gradients $-$ Towards High Throughput Ab Initio Molecular Dynamics [0.0]
  This article presents an optimized algorithm and implementation for calculating resolution-of-the-identity Hartree-Fock energies and analytic gradients using multiple Graphics Processing Units (GPUs) The algorithm is especially designed for high throughput emphab initio molecular dynamics simulations of small and medium size molecules (10-100 atoms)
  arXiv  Detail & Related papers  (2024-07-29T00:14:10Z)
• Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting [55.71424195454963]
  Spec-Gaussian is an approach that utilizes an anisotropic spherical Gaussian appearance field instead of spherical harmonics. Our experimental results demonstrate that our method surpasses existing approaches in terms of rendering quality. This improvement extends the applicability of 3D GS to handle intricate scenarios with specular and anisotropic surfaces.
  arXiv  Detail & Related papers  (2024-02-24T17:22:15Z)
• Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers [65.60007071024629]
  We show that Adaptive SGD outperforms four state-of-the-art solutions in time-to-accuracy. We show experimentally that Adaptive SGD outperforms four state-of-the-art solutions in time-to-accuracy.
  arXiv  Detail & Related papers  (2021-10-13T20:58:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.