TFDM: Time-Variant Frequency-Based Point Cloud Diffusion with Mamba

2025-03-17

AI Summary

Addressing the challenges of modeling long-range geometric dependencies and insufficient detail recovery in 3D point cloud generation, this paper introduces the Mamba state-space model into a diffusion framework for the first time, proposing a time-varying frequency diffusion generative method. Our approach features: (1) a latent-space Dual-Mamba Block (DM-Block), which efficiently captures long-range dependencies in serialized point clouds while reducing parameters by 10× and inference latency by 9×; and (2) a Time-Adaptive Frequency Encoder (TF-Encoder), which dynamically emphasizes critical frequency bands during late-stage denoising to enhance local details. Integrating space-filling curve serialization, frequency-domain attention, and a U-Net architecture, our method achieves state-of-the-art performance on ShapeNet-v2: 1-NNA-Abs50 EMD = 0.14% and COV EMD = 57.90%, significantly outperforming existing diffusion-based and autoregressive approaches.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Diffusion models currently demonstrate impressive performance over various generative tasks. Recent work on image diffusion highlights the strong capabilities of Mamba (state space models) due to its efficient handling of long-range dependencies and sequential data modeling. Unfortunately, joint consideration of state space models with 3D point cloud generation remains limited. To harness the powerful capabilities of the Mamba model for 3D point cloud generation, we propose a novel diffusion framework containing dual latent Mamba block (DM-Block) and a time-variant frequency encoder (TF-Encoder). The DM-Block apply a space-filling curve to reorder points into sequences suitable for Mamba state-space modeling, while operating in a latent space to mitigate the computational overhead that arises from direct 3D data processing. Meanwhile, the TF-Encoder takes advantage of the ability of the diffusion model to refine fine details in later recovery stages by prioritizing key points within the U-Net architecture. This frequency-based mechanism ensures enhanced detail quality in the final stages of generation. Experimental results on the ShapeNet-v2 dataset demonstrate that our method achieves state-of-the-art performance (ShapeNet-v2: 0.14% on 1-NNA-Abs50 EMD and 57.90% on COV EMD) on certain metrics for specific categories while reducing computational parameters and inference time by up to 10$ imes$ and 9$ imes$, respectively. Source code is available in Supplementary Materials and will be released upon accpetance.

Problem

• Enhances 3D point cloud generation using Mamba state-space models.
• Reduces computational overhead with latent space processing.
• Improves detail quality via time-variant frequency encoding.

Innovation

• Dual latent Mamba block for 3D point cloud generation
• Time-variant frequency encoder enhances detail quality
• Space-filling curve optimizes Mamba state-space modeling