LiFi：基于多视角图像的 LiDAR 点云生成框架
LiFi: LiDAR Generation from Multi-View Images via Geometric and Semantic Collaborative Guidance
一篇论文，用多视角图像生成 LiDAR 点云，靠 DepthAnything3 加双分支 CFG 提升几何和语义对齐，做自动驾驶数据合成的可以看看。
论文提出 LiFi（LiDAR From images），一个从多视角相机图像生成 LiDAR 点云的专用框架。该框架通过几何和语义两个分支引导潜在扩散过程：几何分支结合 DepthAnything3 和不确定性感知编码器构建场景特征，语义分支通过跨视角交互模块提供高层语义表示。作者还引入双分支平衡的 classifier-free guidance（CFG）策略，在增强条件生成能力的同时保留两分支的独立学习与协同可控性。实验显示 LiFi 在生成高保真 LiDAR 场景上超过现有 state-of-the-art 方法，代码将发布在 GitHub。
LiFi: LiDAR Generation from Multi-View Images via Geometric and Semantic Collaborative Guidance
Generating LiDAR point clouds from multi-view camera images is a valuable task with applications in controllable data synthesis, cross-modal simulation and various perception tasks. However, most existing methods are not specifically designed for this task, and the quality of their generated LiDAR remains limited. Moreover, these methods typically focus only on semantic cues in images or generate LiDAR from a monocular camera image. In this work, we introduce LiFi (LiDAR From images), a dedicated framework for generating visually and geometrically aligned LiDAR scenes from multi-view camera images. LiFi guides the latent diffusion process through two complementary branches: geometry and semantics. The geometry branch constructs scene features from images through a reverse sampling algorithm and an uncertainty-aware geometric encoder combined with DepthAnything3, whereas the semantic branch provides high-level semantic representations via a cross-view interaction module. We further introduce a dual-branch balanced classifier-free guidance (CFG) strategy, which enhances the model's conditional generation capability while preserving the independent learning and collaborative controllability of the two branches. Extensive experiments demonstrate that LiFi outperforms state-of-the-art methods in generating high-fidelity LiDAR scenes. The code will be publicly available at: https://github.com/szzhou88/LiFi.
