3D Shape Generation through Point Transformer Diffusion
Ladda ner
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Publicerad
2023
Författare
Lan, Ji
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Diffusion models are a novel class of generative models, which have demonstrated
promising results in 3D point cloud generation. Meanwhile, Transformer-based models
have achieved impressive outcomes across various benchmarks in the field of 3D
point cloud understanding. However, the integration of a Transformer based on the
local self-attention mechanism with a diffusion model for 3D point cloud generation
remains unexplored. In this work, we propose Point Transformer Diffusion (PTD), a
probabilistic and flexible generative model. PTD integrates the standard Denoising
Diffusion Probabilistic Model, adapted for 3D data, with Point Transformer, a local
self-attention network specifically designed for 3D point clouds. To enhance PTD’s
performance, several adjustments and techniques are implemented to align the Point
Transformer model more effectively with the diffusion model and with the specific
generation task. Experiments demonstrate PTD’s ability to generate realistic and
diverse shapes. Furthermore, the evaluation results of PTD are comparable to, and
in some cases even marginally superior to, those achieved by Point-Voxel Diffusion,
which is a state-of-the-art approach. We hope that our work will inspire future
investigations into architectures that combine diffusion models and Transformers,
along with their application to a wide range of 3D generation tasks.
Beskrivning
Ämne/nyckelord
3D point clouds, 3D shape generation, diffusion models, Transformers