LiFi
A framework that generates LiDAR point clouds from multi-view camera images. Through two cooperating branches, a geometry branch and a semantic branch, it guides a latent diffusion model to produce LiDAR scene generation in which visual and geometric properties are aligned.
Timeline (last 90 days)
The paper "LiFi: LiDAR Generation from Multi-View Images via Geometric and Semantic Collaborative Guidance" proposes the LiFi framework for generating LiDAR point clouds from multi-view camera images.
LiFi jointly guides the latent diffusion process through two complementary branches, a geometric branch and a semantic branch, to generate LiDAR scenes that are visually and geometrically aligned.
LiFi's geometric branch adopts a reverse sampling algorithm and incorporates an uncertainty-aware geometric encoder that integrates the depth estimation capability of DepthAnything3.
LiFi's semantic branch establishes associations between different views through a cross-view interaction module, capturing the semantic consistency of the scene.
LiFi introduces a dual-branch balanced classifier-free guidance strategy, setting adjustable guidance coefficients for geometry and semantics separately.
The paper states that extensive experiments show LiFi outperforms existing state-of-the-art methods in generating high-fidelity LiDAR scenes.
LiFi's code will be made public on GitHub, with the repository address https://github.com/szzhou88/LiFi.
LiFi targets LiDAR point cloud generation, adopts a latent diffusion framework, and the conditional information provided by the geometric and semantic dual branches is injected into every layer of the denoising network.
All Facts (8)
LiFi targets LiDAR point cloud generation, adopts a latent diffusion framework, and the conditional information provided by the geometric and semantic dual branches is injected into every layer of the denoising network.
50%UnverifiedLiFi jointly guides the latent diffusion process through two complementary branches, a geometric branch and a semantic branch, to generate LiDAR scenes that are visually and geometrically aligned.
50%UnverifiedLiFi's geometric branch adopts a reverse sampling algorithm and incorporates an uncertainty-aware geometric encoder that integrates the depth estimation capability of DepthAnything3.
50%UnverifiedLiFi's semantic branch establishes associations between different views through a cross-view interaction module, capturing the semantic consistency of the scene.
50%UnverifiedLiFi introduces a dual-branch balanced classifier-free guidance strategy, setting adjustable guidance coefficients for geometry and semantics separately.
50%UnverifiedThe paper states that extensive experiments show LiFi outperforms existing state-of-the-art methods in generating high-fidelity LiDAR scenes.
50%UnverifiedLiFi's code will be made public on GitHub, with the repository address https://github.com/szzhou88/LiFi.
50%UnverifiedThe paper "LiFi: LiDAR Generation from Multi-View Images via Geometric and Semantic Collaborative Guidance" proposes the LiFi framework for generating LiDAR point clouds from multi-view camera images.
50%