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Found 5 papers, 3 papers with code
Fast LiDAR Upsampling using Conditional Diffusion Models
Existing approaches have shown the possibilities for using diffusion models to generate refined LiDAR data with high fidelity, although the performance and speed of such methods have been limited.
LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models
In this work, we present R2DM, a novel generative model for LiDAR data that can generate diverse and high-fidelity 3D scene point clouds based on the image representation of range and reflectance intensity.
Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data
We demonstrate the fidelity and diversity of our model in comparison with the point-based and image-based state-of-the-art generative models.
Isomorphic mesh generation from point clouds with multilayer perceptrons
Moreover, the unified mesh structure of isomorphic meshes enables the same process to be applied to all isomorphic meshes; although in the case of general mesh models, we need to consider the processes depending on their mesh structures.
Learning to Drop Points for LiDAR Scan Synthesis
As in the related studies, we process LiDAR data as a compact yet lossless representation, a cylindrical depth map.
