题目:DiffusionPCR: Diffusion Models for Robust Multi-Step Point Cloud Registration 名称:DiffusionPCR:稳健多步点云配准的扩散模型 论文:arxiv.org/abs/2312.0305 代码: 单位:华中科技大学 15.Speech/语音 FastDiff 题目:FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis 名称:Fas...
介绍一下我们最新开源的工作:FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators. 给定部分重叠的图像和,FreeReg能够估计可靠的像素-三维点同名关系并解算图像-点云相对位姿关系。值得注意的是,FreeReg不需要任何训练/微调! 基于FreeReg估计的准确的同名关系...
To address this issue, we propose a decomposed latent diffusion model that separately captures consistency information and offset information in the latent space with feature decoupling. To learn effective consistency information, the consistency constraint among different point clouds with a shape is ...
Diffusion Probabilistic Models for 3D Point Cloud GenerationShitong Luo, Wei Hu*Wangxuan Institute of Computer TechnologyPeking University{luost, forhuwei}@pku.edu.cnAbstractWe present a probabilistic model for point cloud gen-eration, which is fundamental for various 3D vision taskssuch as shape c...
Collecting 3D point cloud data is cumbersome, so generating high-quality point clouds from existing data can save time and resources while providing more data to support tasks in various fields. In this paper, we propose a neighborhood feature enhancement flow diffusion model for point cloud ...
《Diffusion probabilistic models for 3d point cloud generation》是一项关于基于扩散的三维视觉任务的早期工作。在非平衡热力学的激励下,这项工作将点云中的点类比为热力学系统中的粒子,并在点云生成中采用了扩散过程,从而获得了具有竞争力的性能。 PVD是一项同时进行的基于扩散的点云生成工作,但在没有附加形状编码...
《Diffusion probabilistic models for 3d point cloud generation》是一项关于基于扩散的三维视觉任务的早期工作。在非平衡热力学的激励下,这项工作将点云中的点类比为热力学系统中的粒子,并在点云生成中采用了扩散过程,从而获得了具有竞争力的性能。 PVD是一项同时进行的基于扩散的点云生成工作,但在没有附加形状编码...
1定义Diffusion Model就像是通过逐步“模糊”和“清晰化”图片来生成新图像的技术。想象一下从雾中逐渐...
We present a probabilistic model for point cloud generation, which is critical for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation. Inspired by the diffusion process in non-equilibrium thermodynamics, we view points in point clouds as particles in a ther...
larger than 400. Less point clouds mean that it is less likely to find similar point clouds in the validation set for a generated point cloud. Hence, it would lead to a worse Minimum-Matching-Distance (MMD) score even if we renormalize the shapes during the validation stage in the training...