Wenqi Jia*
Zhewen Hu*
Ying Huang*
Yu Gong
UT Arlington [email protected]
Texas A&M University [email protected]
UT Arlington [email protected]
Independent Researcher [email protected]
Stavros Kalafatis
Yuke Wang
Wei Niu
Chengming Zhang
Texas A&M University [email protected]
Rice University [email protected]
University of Georgia [email protected]
University of Houston [email protected]
Ang Li
Sheng Di
Yuede Ji
Bo Fang
University of Washington [email protected]
Argonne National Labs [email protected]
UT Arlington [email protected]
UT Arlington [email protected]
Miao Yin†
3D Gaussian Splatting (3DGS) enables high-fidelity and realtime 3D scene reconstruction, but scaling training to largescale scenes requires optimizing hundreds of millions of Gaussians across multiple GPUs. Existing distributed approaches either partition scenes into isolated regions, causing global inconsistency, or rely on global Gaussian-level exchanges, which lead to substantial growth in inter-GPU communication and quickly dominate iteration time. We propose Splaxel, a communication-efficient distributed 3DGS training framework based on pixel-level local rendering and global composition. Instead of synchronizing Gaussians, each GPU renders its local subset and exchanges only partial pixel values, maintaining mathematical consistency while keeping communication cost stable as the scene size increases. Splaxel further reduces pixel-level redundancy through geometric and transmittance visibility prediction and improves GPU utilization via conflict-free camera-view consolidation. Evaluated on large-scale datasets with up to 120M Gaussians, Splaxel achieves up to 7.6× speedup over the state-of-the-art distributed 3DGS framework while preserving high reconstruction quality.
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Abstract
Local Projection
Gaussian Communication
Global Blending & Rendering
GPU 1
UT Arlington [email protected]
Local Projection
Gaussian Communication
Global Blending & Rendering
GPU 0
(a) Gaussians partition (b) Existing distributed training with Gaussian-level communication Local Projection
Local Blending & Rendering
Pixel Communication
Global Composition
GPU 1
arXiv:2606.18588v1 [cs.DC] 17 Jun 2026
Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication
Local Projection
Local Blending & Rendering
Pixel Communication
Global Composition
(c) Proposed distributed training with pixel-level communication
Figure 1. Conceptual comparison between pixel-level communication and Gaussian-level communication schemes. fine geometric and appearance detail is critical [30]. To facilitate such a process, 3D Gaussian Splatting (3DGS) [27, 46, 54] has emerged as the dominant solution for high-fidelity, realtime scene rendering. 3DGS trains by projecting each 3D Gaussian, defined by its position, covariance, color, and opacity, onto the target camera plane, where they are depth-sorted and splat-rendered with alpha blending to form the image. The rendering loss is then backpropagated through the splatting pipeline to update all Gaussian parameters [27, 36]. By optimizing millions of those explicit 3D Gaussians, 3DGS captures rich spatial details and achieves significantly higher rendering efficiency compared to conventional neural implicit representations [32, 35, 48, 55]. Despite its rendering efficiency, scaling 3DGS to large scenes is fundamentally challenging due to its dynamic, viewdependent access patterns and the resulting inefficiencies in distributed training [62, 71, 73, 77]. To scale up 3DGS for large-scale 3D scene reconstruction [5, 25, 29], the primary
Introduction
3D scene reconstruction supports a wide range of applications, including autonomous driving [38, 51, 78, 80], digital twins [9], and augmented reality [16, 26], where modeling * Equal contribution. † Corresponding author. 1
Jia and Yin, et al. Update parameters {𝝁, 𝜮, 𝒄, 𝑜}
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𝐿(𝛩) = 𝐿!"# + 𝜆𝐿$%&&'( 4
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Parameter Optimization Re
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Sorted by depth 𝑑
Figure 2. The standard training pipeline of 3D Gaussian Splatting. solution is to leverage distributed training to break the memory barrier of optimizing massive numbers of Gaussians. However, 3DGS exhibits a dynamic and imbalanced access pattern that depends on the current random camera view, where it activates distinct subsets of Gaussians in different views [52, 74]. Moreover, the increased number of Gaussians in large-scale scenes significantly intensifies this pattern. Consequently, existing distributed training frameworks for deep neural networks (DNNs), e.g., DeepSpeed [53], PyTorch DDP [37], Alpha [76], which assume static and balanced tensor computations, are incompatible with 3DGS. To tackle these challenges, existing efforts can be broadly categorized into two main directions. i) Algorithm optimization. Existing works [7, 28, 41, 43] in this category leverage divide-and-conquer strategies, partitioning the scene into independent regions, fully optimizing each region separately across GPUs, and finally merging them for complete reconstruction. While these works can optimize Gaussian parameters locally with the dynamic camera views, they sacrifice global consistency. Consequently, it leads to visible discontinuities across the reconstructed scene as each partition optimizes its Gaussians in isolation without considering neighboring regions. ii) System optimization. In this direction, Grendel [75] is the only work that globally preserves the 3DGS pipeline and consistency. Grendel randomly distributes Gaussians across devices, where each device determines the required Gaussians based on the current camera view and requests the missing Gaussians from other devices through all-to-all communication. While this design maintains global consistency, it faces significant scalability challenges in large-scale scenes. As the number of Gaussians grows, such a global Gaussian-level exchange becomes increasingly expensive, leading to a substantial increase in the communication ratio for inter-GPU data transfer. Consequently, communication dominates training iteration time and severely limits 3DGS’s scalability and throughput. To address the above issues, we consider the opportunity of a pixel-level communication scheme, where each GPU renders locally based on its local Gaussian partition and exchanges only the rendered partial pixels for global blending, as shown in Fig. 1. Pixel-level communication enables constant data transmission volume across GPUs, regardless of the number of Gaussians involved. Such property alleviates the burden of substantial Gaussians in large-scale scenes.
Despite the potential to mitigate communication bottleneck, three main challenges exist. i) Global inconsistency. At the pixel level, local rendering lacks the global Gaussian information, which may lead to a Gaussian disorder in the blending phase. ii) Pixel-level redundancy. A naive pixel-level communication scheme leads to spatial redundancy and saturation redundancy due to geometric invisibility and transmittance saturation, respectively, resulting in a majority of transmissions being zeros, which implies significant bandwidth waste on data that never affects the rendering result. iii) Insufficient GPU utilization. Most camera views only cross a subset of the Gaussian partitions, but all GPUs must participate in the computation for a single view under the naive scheduling strategy. This leads to a high GPU idle rate. In this paper, we propose Splaxel, enabling efficient distributed 3DGS training for large-scale scene reconstruction via pixel-level communication. Splaxel leverages convexbased partition to ensure the global consistency. It reduces spatial redundancy by projecting the vertices of overlapping 3D space between the view frustum and the convex Gaussian partition onto the rendering plane without extra communication. Moreover, Splaxel enhances GPU utilization through an efficient conflict-free camera view and GPU scheduling mechanism. Our contributions are summarized as follows:
• We analyze the bottleneck (§3.1) in the existing distributed 3DGS training framework with Gaussianlevel communication, identifying the opportunities and challenges of a pixel-level communication scheme. • We propose a principled 3DGS distributed training framework based on pixel-level communication (§4.2), with a reformulation of the Gaussian rendering and parameters update paradigm to maintain mathematical consistency. • We present two novel techniques (§4.3, §4.4) that reduce pixel-level redundancy by predicting geometric and transmittance invisibility, and improve GPU utilization with conflict-free camera view consolidation. • We conduct extensive evaluations (§5) on multiple large-scale datasets, including the largest publicly available ones, Big City Street and Aerial, with 120M Gaussians, on which Splaxel outperforms the existing SOTA by 7.6× and 2.9× speedups with 8 GPUs, respectively. 2