WorldMesh 类似的开源 3D 场景生成项目调研与源码收集，待 GPU 后复现
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+ 从 WorldMesh 出发，梳理当前最优秀的开源 3D 场景生成项目。
+ 文本/图像输入 → 可导航的 3D 世界。源码已本地保存，待 GPU 后逐一复现。
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+ From WorldMesh to the best open-source 3D scene generation projects.
+ Text/Image → Navigable 3D Worlds. All source code saved locally, ready for GPU.
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++ WorldMesh 提出了几何优先的 3D 场景生成思路，但代码未开源。我们找到了 7 个可复现的替代方案。 + The geometry-first approach that sparked this research. Code not yet released — here are 7 reproducible alternatives. +
+ ++ + 核心思路：几何优先（Geometry-First）— 文本 → 平面图 → 3D 网格支架（墙面、地面、结构）→ + 基于网格条件的图像扩散合成外观 → 3D Gaussian Splatting 输出可导航场景。 + 支持大规模多房间生成，古罗马到赛博朋克多种风格。用户偏好测试 96.2% 优于基线。 + + + Core Idea: Geometry-First — Text → Floor Plan → 3D Mesh Scaffold (walls, floors, structure) → + Mesh-Conditioned Image Diffusion for appearance → 3D Gaussian Splatting for navigable output. + Supports large-scale multi-room generation across styles (Ancient Roman to Cyberpunk). 96.2% user preference over baselines. + +
++ 代码完整发布，社区验证，文档齐全 + Complete code, community-verified, well-documented +
+ ++ + 文本/图像 → 可导航 3D 场景。基于全景生成 + Gaussian Splatting，pip 安装即用。 + 支持低显存模式（10GB），是目前上手门槛最低的方案。 + + + Text/Image → navigable 3D scene. Panorama generation + Gaussian Splatting, pip install ready. + Low VRAM mode (10GB) makes it the lowest barrier option available. + +
++ + 该领域的奠基之作。文本 → 带纹理的 3D 房间网格。迭代生成视图、修复、对齐深度、融合网格。 + 代码干净稳定，复现性经过大量验证。 + + + The foundational work. Text → textured 3D room mesh. Iterative view generation, inpainting, depth alignment, and mesh fusion. + Clean, stable codebase with proven reproducibility. + +
++ + 普林斯顿出品，程序化生成照片级室内外 3D 场景。100% 程序化，无需外部素材。 + 最成熟的项目（3,214 commits），Mac CPU 也能跑。适合生成训练数据集。 + + + Princeton's procedural generation framework. Photorealistic indoor + outdoor 3D scenes, 100% procedural, no external assets. + Most mature project (3,214 commits). CPU compatible — works on Mac without NVIDIA GPU. + +
++ + 最接近 WorldMesh 的方案。单张图像 → 连通的可导航 3D 场景，基于 Fast Layered Gaussian Surfels (FLAGS)。 + 支持浏览器交互式导航，每个新视角 <10 秒。 + + + Closest to WorldMesh. Single image → connected navigable 3D scenes via Fast Layered Gaussian Surfels (FLAGS). + Browser-based interactive navigation, <10s per new view. + +
++ 代码可用，构建流程较复杂或硬件要求更高 + Code available, more complex setup or higher hardware requirements +
+ ++ + 文本 → 分层 360° 全景 3D 场景 + Gaussian Splatting。沉浸感最强。 + 需编译 C++ 扩展（Ceres solver、360monodepth），构建过程较复杂。 + + + Text → layered 360° panoramic 3D scene + Gaussian Splatting. Most immersive experience. + Requires C++ extension compilation (Ceres solver, 360monodepth). + +
+ ++ + 文本 → 3D 场景（Gaussian Splatting），用户偏好测试 88-95%。 + 提供预生成输出可跳过耗时阶段，Stage 2 训练约需数小时。 + + + Text → 3D scene via Gaussian Splatting. 88-95% user preference in studies. + Pre-generated outputs available to skip slow stages. Stage 2 training takes several hours. + +
+ ++ + 文本 + 空间布局 → 多房间公寓 3D 场景（NeRF）。支持复杂平面图。 + 多房间能力最接近 WorldMesh，但需要 2+ GPU。 + + + Text + spatial layout → multi-room apartment 3D scenes via NeRF. Supports complex floor plans. + Closest to WorldMesh's multi-room capability, but requires 2+ GPUs. + +
+ ++ 根据你的场景和硬件选择最合适的项目 + Pick the right project based on your scenario and hardware +
+ +pip 安装，最低 10GB 显存，文本直接出 3D 场景pip install, 10GB VRAM minimum, text directly to 3D scene
+ WorldGen +ICCV 2023 奠基之作，代码干净，论文被引最多ICCV 2023 foundational work, clean code, most cited
+ Text2Room +CPU 也能跑，基于 Blender 程序化生成，Mac 可用CPU compatible, Blender-based procedural generation, works on Mac
+ Infinigen +交互式导航，连通多房间，但需要 48GB 显存Interactive navigation, connected multi-room, needs 48GB VRAM
+ WonderWorld +SIGGRAPH 级别质量，360 度环绕 3D 场景SIGGRAPH-level quality, 360-degree surround 3D scenes
+ LayerPano3D +支持平面图控制，最精确的空间布局方案Floor plan controlled, most precise spatial layout
+ SceneCraft +
+ 所有源码已 clone 至 repos/ 目录，待 GPU 后逐一复现
+ All repos cloned to repos/ — ready for GPU
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| 项目Project | +本地路径Local Path | +Stars | +会议Venue | +GPU | +状态Status | +
|---|---|---|---|---|---|
| WorldGen | +repos/WorldGen/ |
+ 1,592 | +Independent | +10-24GB | +已保存Saved | +
| Text2Room | +repos/text2room/ |
+ 1,082 | +ICCV 2023 | +16-24GB | +已保存Saved | +
| Infinigen | +repos/infinigen/ |
+ 6,878 | +CVPR 23+24 | +CPU OK | +已保存Saved | +
| WonderWorld | +repos/WonderWorld/ |
+ 717 | +CVPR 2025 | +48GB | +已保存Saved | +
| LayerPano3D | +repos/LayerPano3D/ |
+ 315 | +SIGGRAPH 25 | +16-24GB | +已保存Saved | +
| RealmDreamer | +repos/realmdreamer/ |
+ 297 | +3DV 2025 | +CUDA 11.8 | +已保存Saved | +
| SceneCraft | +repos/SceneCraft/ |
+ 233 | +NeurIPS 24 | +2+ GPU | +已保存Saved | +
+ 3D 场景生成领域近年关键节点 + Key milestones in 3D scene generation +
+ ++ 进一步学习和追踪 + Further reading and tracking +
+ + +