作者 | 微卷的大白 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/1952449084788029155 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 前两天看到李飞飞 Worldlabs 新工作Mrable的时候，提到后面想多看一看 3DGS / 重建相关的工作。 不过如果真的有小白要踩坑 ，gsplat 的文档和维护其实比gaussian-splatting 要稍微好一些，个人更推荐这个库。 相比3DGS 论文对应的 gaussian-splatting 库，nerfstudio-projectgsplat 是对官方库做了一些优化，可参考https://docs.gsplat.studio/main/migration/migration_inria.html 的 说明。 但是知乎搜了一下发现，讲 3DGS 论文原理、改进的不少，我自己上半年也回顾过cuda kernel 源码：重温经典之 3DGS CUDA 源码解析 ，但是另一个常用的gsplat ...

Core Viewpoint - The article emphasizes the establishment of a leading communication platform for autonomous driving technology in China, focusing on industry, academic, and career development aspects [1]. Group 1 - The platform, named "Autonomous Driving Heart," aims to facilitate discussions and exchanges among professionals in various fields related to autonomous driving technology [1]. - The technical discussion group covers a wide range of topics including large models, end-to-end systems, VLA, BEV perception, multi-modal perception, occupancy, online mapping, 3DGS, multi-sensor fusion, transformers, point cloud processing, SLAM, depth estimation, trajectory prediction, high-precision maps, NeRF, planning control, model deployment, autonomous driving simulation testing, product management, hardware configuration, and AI job exchange [1]. - Interested individuals are encouraged to join the community by adding a WeChat assistant and providing their company/school, nickname, and research direction [1].

Core Insights - The article discusses the advancements in 3D scene generation, highlighting a comprehensive survey that categorizes existing methods into four main paradigms: procedural methods, neural network-based 3D representation generation, image-driven generation, and video-driven generation [2][4][7]. Summary by Sections Overview of 3D Scene Generation - A survey titled "3D Scene Generation: A Survey" reviews over 300 representative papers and outlines the rapid growth in the field since 2021, driven by the rise of generative models and new 3D representations [2][4][5]. Four Main Paradigms - The four paradigms provide a clear technical roadmap for 3D scene generation, with performance metrics compared across dimensions such as realism, diversity, viewpoint consistency, semantic consistency, efficiency, controllability, and physical realism [7]. Procedural Generation - Procedural generation methods automatically construct complex 3D environments using predefined rules and constraints, widely applied in gaming and graphics engines. This category can be further divided into neural network-based generation, rule-based generation, constraint optimization, and large language model-assisted generation [8]. Image-based and Video-based Generation - Image-based generation leverages 2D image models to reconstruct 3D structures, while video-based generation treats 3D scenes as sequences of images, integrating spatial modeling with temporal consistency [9]. Challenges in 3D Scene Generation - Despite significant progress, challenges remain in achieving controllable, high-fidelity, and physically realistic 3D modeling. Key issues include uneven generation capabilities, the need for improved 3D representations, high-quality data limitations, and a lack of unified evaluation standards [10][16]. Future Directions - Future advancements should focus on higher fidelity generation, parameter control, holistic scene generation, and integrating physical constraints to ensure structural and semantic consistency. Additionally, supporting interactive scene generation and unifying perception and generation capabilities are crucial for the next generation of 3D modeling systems [12][18].