Update from Sync Service
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FNS Service
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---
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title: "Introducing 3D Gaussian Splats with Hierarchical Level of Detail Using 3D Tiles"
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source: "https://cesium.com/blog/2026/04/27/3d-gaussian-splats-lod/"
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author:
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- "[[Shehzan Mohammed]]"
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published: 2026-04-27
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created: 2026-05-11
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description: "3D Gaussian splats are now available in the Cesium and 3D Tiles ecosystems, including CesiumJS, Cesium for Unreal, and Cesium ion."
|
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tags:
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- "clippings"
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---
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3D Gaussian splats are now available in the Cesium and 3D Tiles ecosystems, including CesiumJS, Cesium for Unreal, and Cesium ion. This integration enables developers to stream and visualize high-fidelity, photorealistic 3D content that captures the real world with unprecedented detail. By leveraging 3D Tiles as a spatial index and glTF as the payload, massive 3D Gaussian splat datasets can now be streamed with level of detail (LOD), ensuring high performance from city-scale environments down to sub-centimeter details.
|
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|
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3D Gaussian splatting is an efficient radiance field reconstruction method that converts 2D images into a sparse cloud of volumetric 3D Gaussians. Unlike traditional photogrammetry, which often struggles to represent thin or complex structures, 3D Gaussian splats excel at preserving visual fidelity for cables, power lines, vegetation, and reflective surfaces by creating a point cloud of soft, rounded shapes of the subject, with each point defined by properties including position, orientation, color, and opacity. This technique uses spherical harmonics to support view-dependent lighting, allowing for realistic specular reflections and anisotropy that traditional reality meshes cannot easily replicate.
|
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<iframe src="https://player.vimeo.com/video/1186727561?app_id=122963" width="426" height="240" frameborder="0" allow="autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share" title="3D Gaussian Splats with Hierarchical Level of Detail Using 3D Tiles"></iframe>
|
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3D Gaussian splatting provides significant advantages for industries requiring high-fidelity reality capture and photorealistic digital twins, including infrastructure monitoring, telecom tower maintenance, and electrical utility and substation inspections—plus the built environment, such as in real estate, where traditional mesh and point cloud methods often have limits. Beyond industrial applications, we see 3D Gaussian splats being adopted in media and entertainment to capture challenging visual details like reflections, semitransparent materials, and fire. 3D Gaussian splats are expected to play a key role in long-term asset monitoring and situational awareness, enabling users to track changes in the natural and built environment over the entire life cycle.
|
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|
||||
|
||||
|
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3D Gaussian splats enable high-fidelity accurate visualizations of thin-and-long assets like cranes, building materials, power stations, and cell towers. This crane is depicted with Cesium for Unreal.
|
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|
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|
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3D Gaussian splats enable high-fidelity accurate visualizations of thin-and-long assets like cranes, building materials, power stations, and cell towers. This scene is depicted in CesiumJS.
|
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|
||||
|
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3D Gaussian splats enable high-fidelity accurate visualizations of thin-and-long assets like cranes, building materials, power stations, and cell towers. This scene is depicted in CesiumJS.
|
||||
|
||||
|
||||
|
||||
3D Gaussian splats enable high-fidelity accurate visualizations of thin-and-long assets like cranes, building materials, power stations, and cell towers. This scene is depicted in CesiumJS.
|
||||
|
||||
|
||||
|
||||
3D Gaussian splats enable high-fidelity accurate visualizations of thin-and-long assets like cranes, building materials, power stations, and cell towers. This scene is depicted in CesiumJS.
|
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|
||||
|
||||
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3D Gaussian splats enable high-fidelity accurate visualizations of thin-and-long assets like cranes, building materials, power stations, and cell towers. This scene is depicted in CesiumJS.
|
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|
||||
## Standardizing 3D Gaussian splats in glTF with SPZ compression
|
||||
|
||||
To ensure 3D Gaussian splats are interoperable across platforms, we have collaborated with Khronos, Open Geospatial Consortium (OGC), Esri, and Niantic Spatial to integrate this new graphics primitive into the glTF 3D asset standard. This effort centers on two new extensions: [KHR\_gaussian\_splatting](https://github.com/KhronosGroup/glTF/tree/4ecfc3bd8c439a1c3feab04218212e6b9b222253/extensions/2.0/Khronos/KHR_gaussian_splatting) and [KHR\_gaussian\_splatting\_compression\_spz](https://github.com/KhronosGroup/glTF/pull/2531). The base glTF extension defines how splats are stored as point primitives, which allows for a graceful fallback to sparse point clouds in renderers that do not yet support rendering 3D Gaussian splats.
|
||||
|
||||
A major challenge for 3D Gaussian splats has been their massive file size, which can hinder efficient streaming. To solve this, we adopted the SPZ compression format, an open source solution provided by Niantic Spatial. SPZ uses advanced quantization techniques and gzip to compress splat data by up to 90% compared to standard PLY files while maintaining visual fidelity. This format is particularly effective at handling spherical harmonic data for specular lighting. Furthermore, the recent SPZ 2.0.0 release introduced improved rotational accuracy for linear geospatial features—such as antennas and power lines—by encoding rotations as normalized quaternions.
|
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|
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3D Gaussian splats will be added to 3D Tiles as part of the proposed 3D Tiles 2.0 OGC community standard.
|
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|
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## Creating 3D Gaussian splats using Cesium ion
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|
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With the integration of iTwin Capture technology into Cesium ion, the platform provides an end-to-end pipeline for reality modeling, including the creation of 3D Gaussian splats with LOD. Users can upload source photos directly to Cesium ion, which will automatically reconstruct a 3D Tileset as a mesh, point cloud, or 3D Gaussian splats.
|
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|
||||
To accommodate different development needs, these workflows are accessible through both the Cesium ion web interface and via REST APIs, enabling everything from manual one-off uploads to fully automated, high-scale production pipelines. Once tiled, these splats are georeferenced, allowing them to be instantly combined with global datasets like Cesium World Terrain or Google Photorealistic 3D Tiles for full geospatial context.
|
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3D Gaussian splat tileset of Microsoft Redmond Campus, as viewed in CesiumJS. Captured in partnership with Bentley Systems. This tileset uses 20,169 photos (427.7 gigapixels, DJI FC6310 camera) and covers an area of about 3.7 sq km, with mean ground sampling distance 3 cm. The reconstruction is 110 million splats.
|
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|
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## Streaming to CesiumJS, Cesium for Unreal, and more
|
||||
|
||||
CesiumJS now supports 3D Gaussian splat tilesets. The implementation uses specialized shaders for volumetric rendering and high-performance sorting using WebAssembly for increased performance. Developers using [CesiumJS](https://cesium.com/platform/cesiumjs/) can build high-fidelity digital twin applications leveraging 3D Gaussian splat tilesets in conjunction with previously supported data types.
|
||||
|
||||
For developers building immersive experiences with game engines, [Cesium for Unreal](https://cesium.com/platform/cesium-for-unreal/) now supports 3D Gaussian splat tilesets, as well. This enables the integration of massive 3D Gaussian splat dataset rendering into the Unreal Engine ecosystem with the ability to stream 3D Gaussian splats with LOD directly from Cesium ion. In addition to Cesium for Unreal, we plan to add support to Cesium for Unity and other runtime engines based on community feedback. By standardizing splat delivery through 3D Tiles and glTF extensions, we’re ensuring that these assets remain interoperable across platforms as the field of neural rendering evolves.
|
||||
|
||||
## Getting started with 3D Gaussian splats
|
||||
|
||||
Upload your own photos to Cesium ion by [signing up for a free community account](https://ion.cesium.com/signup). You can try out live examples in the Cesium Sandcastle or start building your own reality models today.
|
||||
@@ -0,0 +1,70 @@
|
||||
---
|
||||
title: "Introducing 3D Gaussian Splats with Hierarchical Level of Detail Using 3D Tiles"
|
||||
source: "https://cesium.com/blog/2026/04/27/3d-gaussian-splats-lod/"
|
||||
author:
|
||||
- "[[Shehzan Mohammed]]"
|
||||
published: 2026-04-27
|
||||
created: 2026-05-11
|
||||
description: "3D Gaussian splats are now available in the Cesium and 3D Tiles ecosystems, including CesiumJS, Cesium for Unreal, and Cesium ion."
|
||||
tags:
|
||||
- "clippings"
|
||||
---
|
||||
3D Gaussian splats are now available in the Cesium and 3D Tiles ecosystems, including CesiumJS, Cesium for Unreal, and Cesium ion. This integration enables developers to stream and visualize high-fidelity, photorealistic 3D content that captures the real world with unprecedented detail. By leveraging 3D Tiles as a spatial index and glTF as the payload, massive 3D Gaussian splat datasets can now be streamed with level of detail (LOD), ensuring high performance from city-scale environments down to sub-centimeter details.
|
||||
|
||||
3D Gaussian splatting is an efficient radiance field reconstruction method that converts 2D images into a sparse cloud of volumetric 3D Gaussians. Unlike traditional photogrammetry, which often struggles to represent thin or complex structures, 3D Gaussian splats excel at preserving visual fidelity for cables, power lines, vegetation, and reflective surfaces by creating a point cloud of soft, rounded shapes of the subject, with each point defined by properties including position, orientation, color, and opacity. This technique uses spherical harmonics to support view-dependent lighting, allowing for realistic specular reflections and anisotropy that traditional reality meshes cannot easily replicate.
|
||||
|
||||
<iframe src="https://player.vimeo.com/video/1186727561?app_id=122963" width="426" height="240" frameborder="0" allow="autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share" title="3D Gaussian Splats with Hierarchical Level of Detail Using 3D Tiles"></iframe>
|
||||
|
||||
3D Gaussian splatting provides significant advantages for industries requiring high-fidelity reality capture and photorealistic digital twins, including infrastructure monitoring, telecom tower maintenance, and electrical utility and substation inspections—plus the built environment, such as in real estate, where traditional mesh and point cloud methods often have limits. Beyond industrial applications, we see 3D Gaussian splats being adopted in media and entertainment to capture challenging visual details like reflections, semitransparent materials, and fire. 3D Gaussian splats are expected to play a key role in long-term asset monitoring and situational awareness, enabling users to track changes in the natural and built environment over the entire life cycle.
|
||||
|
||||
|
||||
|
||||
3D Gaussian splats enable high-fidelity accurate visualizations of thin-and-long assets like cranes, building materials, power stations, and cell towers. This crane is depicted with Cesium for Unreal.
|
||||
|
||||
|
||||
|
||||
3D Gaussian splats enable high-fidelity accurate visualizations of thin-and-long assets like cranes, building materials, power stations, and cell towers. This scene is depicted in CesiumJS.
|
||||
|
||||
|
||||
|
||||
3D Gaussian splats enable high-fidelity accurate visualizations of thin-and-long assets like cranes, building materials, power stations, and cell towers. This scene is depicted in CesiumJS.
|
||||
|
||||
|
||||
|
||||
3D Gaussian splats enable high-fidelity accurate visualizations of thin-and-long assets like cranes, building materials, power stations, and cell towers. This scene is depicted in CesiumJS.
|
||||
|
||||
|
||||
|
||||
3D Gaussian splats enable high-fidelity accurate visualizations of thin-and-long assets like cranes, building materials, power stations, and cell towers. This scene is depicted in CesiumJS.
|
||||
|
||||
|
||||
|
||||
3D Gaussian splats enable high-fidelity accurate visualizations of thin-and-long assets like cranes, building materials, power stations, and cell towers. This scene is depicted in CesiumJS.
|
||||
|
||||
## Standardizing 3D Gaussian splats in glTF with SPZ compression
|
||||
|
||||
To ensure 3D Gaussian splats are interoperable across platforms, we have collaborated with Khronos, Open Geospatial Consortium (OGC), Esri, and Niantic Spatial to integrate this new graphics primitive into the glTF 3D asset standard. This effort centers on two new extensions: [KHR\_gaussian\_splatting](https://github.com/KhronosGroup/glTF/tree/4ecfc3bd8c439a1c3feab04218212e6b9b222253/extensions/2.0/Khronos/KHR_gaussian_splatting) and [KHR\_gaussian\_splatting\_compression\_spz](https://github.com/KhronosGroup/glTF/pull/2531). The base glTF extension defines how splats are stored as point primitives, which allows for a graceful fallback to sparse point clouds in renderers that do not yet support rendering 3D Gaussian splats.
|
||||
|
||||
A major challenge for 3D Gaussian splats has been their massive file size, which can hinder efficient streaming. To solve this, we adopted the SPZ compression format, an open source solution provided by Niantic Spatial. SPZ uses advanced quantization techniques and gzip to compress splat data by up to 90% compared to standard PLY files while maintaining visual fidelity. This format is particularly effective at handling spherical harmonic data for specular lighting. Furthermore, the recent SPZ 2.0.0 release introduced improved rotational accuracy for linear geospatial features—such as antennas and power lines—by encoding rotations as normalized quaternions.
|
||||
|
||||
3D Gaussian splats will be added to 3D Tiles as part of the proposed 3D Tiles 2.0 OGC community standard.
|
||||
|
||||
## Creating 3D Gaussian splats using Cesium ion
|
||||
|
||||
With the integration of iTwin Capture technology into Cesium ion, the platform provides an end-to-end pipeline for reality modeling, including the creation of 3D Gaussian splats with LOD. Users can upload source photos directly to Cesium ion, which will automatically reconstruct a 3D Tileset as a mesh, point cloud, or 3D Gaussian splats.
|
||||
|
||||
To accommodate different development needs, these workflows are accessible through both the Cesium ion web interface and via REST APIs, enabling everything from manual one-off uploads to fully automated, high-scale production pipelines. Once tiled, these splats are georeferenced, allowing them to be instantly combined with global datasets like Cesium World Terrain or Google Photorealistic 3D Tiles for full geospatial context.
|
||||
|
||||
|
||||
|
||||
3D Gaussian splat tileset of Microsoft Redmond Campus, as viewed in CesiumJS. Captured in partnership with Bentley Systems. This tileset uses 20,169 photos (427.7 gigapixels, DJI FC6310 camera) and covers an area of about 3.7 sq km, with mean ground sampling distance 3 cm. The reconstruction is 110 million splats.
|
||||
|
||||
## Streaming to CesiumJS, Cesium for Unreal, and more
|
||||
|
||||
CesiumJS now supports 3D Gaussian splat tilesets. The implementation uses specialized shaders for volumetric rendering and high-performance sorting using WebAssembly for increased performance. Developers using [CesiumJS](https://cesium.com/platform/cesiumjs/) can build high-fidelity digital twin applications leveraging 3D Gaussian splat tilesets in conjunction with previously supported data types.
|
||||
|
||||
For developers building immersive experiences with game engines, [Cesium for Unreal](https://cesium.com/platform/cesium-for-unreal/) now supports 3D Gaussian splat tilesets, as well. This enables the integration of massive 3D Gaussian splat dataset rendering into the Unreal Engine ecosystem with the ability to stream 3D Gaussian splats with LOD directly from Cesium ion. In addition to Cesium for Unreal, we plan to add support to Cesium for Unity and other runtime engines based on community feedback. By standardizing splat delivery through 3D Tiles and glTF extensions, we’re ensuring that these assets remain interoperable across platforms as the field of neural rendering evolves.
|
||||
|
||||
## Getting started with 3D Gaussian splats
|
||||
|
||||
Upload your own photos to Cesium ion by [signing up for a free community account](https://ion.cesium.com/signup). You can try out live examples in the Cesium Sandcastle or start building your own reality models today.
|
||||
169
专业领域/Web3DGS技术调研-汇报总结.md
Executable file
169
专业领域/Web3DGS技术调研-汇报总结.md
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# Web3DGS 技术调研汇报总结
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---
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## 一、核心结论（Executive Summary）
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**推荐方案：SuperSplat / PlayCanvas**
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综合数据处理效率、渲染性能、渲染质量、功能完整性四个维度，SuperSplat 在各项对比中表现最优，建议作为项目的高斯泼溅渲染引擎。
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---
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## 二、技术背景
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### 2.1 什么是 3DGS
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3D Gaussian Splatting（3DGS）是一种革命性的实时辐射场渲染技术，将3D场景表示为大量微小的"高斯形状斑点"（Splat）集合，通过可微光栅化实现实时高质量渲染。相比 NeRF 方法，3DGS 兼具训练速度和渲染速度的优势。
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### 2.2 Web端核心挑战
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| 挑战 | 说明 |
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| ----- | -------------------- |
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| 内存限制 | 浏览器可用内存远小于原生应用 |
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| 带宽限制 | 百万级Splat数据传输成为瓶颈 |
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| 渲染算力 | WebGL/WebGPU并行计算能力受限 |
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| 跨平台兼容 | 桌面、移动、VR设备算力差异巨大 |
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### 2.3 LOD 技术的关键作用
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LOD（细节层级）是解决上述挑战的核心技术，实现：
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- **场景规模扩展**：使亿级Splat超大场景渲染成为可能
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- **设备适配**：根据设备性能动态调整渲染负载
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- **带宽优化**：通过流式加载减少初始等待时间
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- **帧率稳定**：保持恒定渲染复杂度，避免性能抖动
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---
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## 三、主流方案对比
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### 3.1 方案概览
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| 方案 | 类型 | 技术栈 | 特点 |
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| :-------------------------: | :------: | :------------------------: | :-------------------: |
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| **Spark 2.0** (World Labs) | 开源Web渲染器 | Three.js + WebGL2 | 连续LOD、GPU虚拟显存、亿级Splat |
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| **LCC** (XGRIDS) | 商业化SDK | 多平台SDK | 空间分块+LOD耦合、Cesium集成 |
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| **SuperSplat** (PlayCanvas) | 开源编辑器+引擎 | PlayCanvas + WebGL2/WebGPU | SOG格式、碰撞检测、轻量易用 |
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| **Visionary** | 学术研究 | WebGPU + ONNX | 每帧推理、插件式架构 |
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| **gsplat.js** | 开源通用库 | 类Three.js API | 轻量级、基础LOD支持 |
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| **FJD Trion** (丰疆) | 商业软件 | 专有格式 | 需授权，未试用 |
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### 3.2 核心差异对比
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| 维度 | Spark 2.0 | LCC | SuperSplat |
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| --------- | ------------------------ | ---------- | ------------------ |
|
||||
| **渲染预算** | 固定预算（桌面2.5M/移动500K~1.5M） | 分块LOD机制 | 全局预算管理 |
|
||||
| **文件格式** | .RAD（渐进式传输） | .lcc（紧凑编码） | .sog（空间有序） |
|
||||
| **内存管理** | GPU虚拟显存分页 | 压缩+LOD优化 | gsplat.splatBudget |
|
||||
| **LOD类型** | 连续LOD（树切面） | 离散LOD+空间分块 | 离散LOD流式分块 |
|
||||
| **碰撞检测** | 不支持 | 需自行实现 | 内置体素碰撞数据生成 |
|
||||
| **最大场景** | 1亿+Splat | 80万㎡场景验证 | 千万级Splat |
|
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---
|
||||
|
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## 四、实测数据
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||||
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||||
### 4.1 测试场景
|
||||
|
||||
| 场景 | 大小 | 来源 | 类型 |
|
||||
| ---- | ----- | ------- | ----- |
|
||||
| 西北大学 | 2.12G | LCC官方示例 | 外部大场景 |
|
||||
| 书店 | 404M | LCC官方示例 | 内部小场景 |
|
||||
| 教堂 | 776M | LCC官方示例 | 外部小场景 |
|
||||
| 台阶 | 1.38G | 中海达数据 | 外部场景 |
|
||||
|
||||
### 4.2 测试设备
|
||||
|
||||
- **低性能台式机H**：i7-9700 + AMD Radeon R7 430
|
||||
- **高性能台式机**：i9-10900K + RTX3070
|
||||
- **笔记本W**：i7-9700 + AMD Radeon R7 430
|
||||
|
||||
### 4.3 数据处理效率
|
||||
|
||||
| 方案 | 台阶场景处理时间 | 备注 |
|
||||
|------|-----------------|------|
|
||||
| Spark 2.0 | 较长（Rust源码需二次开发） | 部分PLY兼容性有问题 |
|
||||
| LCC | ~10秒（LCCEditor） | 存在视觉跳变问题 |
|
||||
| SuperSplat | 需二次开发（Node.js库） | 支持碰撞数据生成 |
|
||||
|
||||
**碰撞数据生成时间**（笔记本W）：
|
||||
- 书店场景：~2分钟
|
||||
- 教堂场景：~3分40秒
|
||||
|
||||
### 4.4 渲染性能排名
|
||||
|
||||
**相同场景、相同模式、相同设备下：**
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|
||||
1. **SuperSplat** — 性能最佳
|
||||
2. **LCC** — 与SuperSplat基本相当，部分情况略逊
|
||||
3. **Spark 2.0** — 性能表现最差
|
||||
|
||||
> 注：LCC2在不同性能模式下表现基本无变化，可能有额外处理逻辑。
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||||
|
||||
### 4.5 渲染质量对比
|
||||
|
||||
| 问题 | Spark 2.0 | LCC | SuperSplat |
|
||||
|------|-----------|-----|------------|
|
||||
| 视觉跳变 | 无 | 有（旋转视角出现模型漏洞） | 无 |
|
||||
| 性能模式模糊 | 正常 | 模糊 | 性能模式下模糊，标准及以上正常 |
|
||||
| 放大细节 | 可加载清晰细节 | 模糊 | 可加载清晰细节 |
|
||||
|
||||
### 4.6 功能对比
|
||||
|
||||
| 功能 | Spark 2.0 | LCC | SuperSplat |
|
||||
| -------- | --------- | ---------- | ----------- |
|
||||
| 碰撞检测数据生成 | 不支持 | 需自行实现 | 内置支持（体素八叉树） |
|
||||
| 第一人称行走 | 需自行实现 | 有API但需自行实现 | 内置支持 |
|
||||
| 点云渲染 | 不支持 | 不支持 | 引擎支持 |
|
||||
| Cesium集成 | 不支持 | 支持 | 不支持 |
|
||||
|
||||
---
|
||||
|
||||
## 五、技术趋势
|
||||
|
||||
1. **连续LOD替代离散LOD**：消除视觉跳变，实现无缝过渡
|
||||
2. **流式传输标准化**：先展示低精度全局视图，再逐步细化
|
||||
3. **WebGL向WebGPU迁移**：计算着色器开启更复杂LOD算法可能
|
||||
4. **空间分块与LOD深度融合**：特别适合大地图、城市级实景
|
||||
5. **AI驱动智能LOD**：神经网络动态预测最优LOD配置
|
||||
|
||||
---
|
||||
|
||||
## 六、推荐方案及理由
|
||||
|
||||
### 选择 SuperSplat 的核心理由
|
||||
|
||||
1. **渲染性能最优** — 实测FPS最高，CPU/GPU耗时最低
|
||||
2. **碰撞检测内置** — 支持体素碰撞数据生成+第一人称行走
|
||||
3. **无视觉跳变** — 渲染质量稳定，无LCC的旋转漏洞问题
|
||||
4. **传输最快** — LOD数据最小，场景初始化最快
|
||||
5. **开源生态** — MIT协议，PlayCanvas引擎成熟
|
||||
|
||||
### 待解决问题
|
||||
|
||||
| 问题 | 说明 |
|
||||
| ------- | -------------------------------------------- |
|
||||
| LOD生成效率 | @playcanvas/splat-transform 需二次开发，当前为Node.js |
|
||||
| 碰撞数据质量 | 生成数据质量需进一步研究优化 |
|
||||
| 点云渲染 | 引擎支持但Demo未实现 |
|
||||
| GIS大场景 | Cesium对3DGS LOD支持不足，仅LCC支持Cesium集成 |
|
||||
| 浏览器适配 | 不同手机和PC 浏览器兼容有差异 |
|
||||
| | |
|
||||
|
||||
---
|
||||
|
||||
## 七、Hi-LiDAR 数据处理发布流程
|
||||
|
||||
已基于 @playcanvas/splat-transform 搭建3DGS在线数据处理和演示系统：
|
||||
|
||||
1. **数据接收** — 接收Hi-LiDAR推送 / 支持模型上传
|
||||
2. **LOD处理** — 支持自动/手动生成LOD数据
|
||||
3. **模型管理** — 在线预览和发布管理
|
||||
4. **分享能力** — 链接分享 + 二维码扫码在线查看
|
||||
5. **用户管理** — 集成中海达会员系统
|
||||
|
||||
**Demo演示地址**：http://172.16.36.73:8092/
|
||||
|
||||
---
|
||||
|
||||
*汇报完毕，请审阅。*
|
||||
Reference in New Issue
Block a user