  Recent advancements in sequence modeling have led to the development of the
Mamba architecture, noted for its selective state space approach, offering a
promising avenue for efficient long sequence handling. However, its application
in 3D shape generation, particularly at high resolutions, remains
underexplored. Traditional diffusion transformers (DiT) with self-attention
mechanisms, despite their potential, face scalability challenges due to the
cubic complexity of attention operations as input length increases. This
complexity becomes a significant hurdle when dealing with high-resolution voxel
sizes. To address this challenge, we introduce a novel diffusion architecture
tailored for 3D point clouds generation-Diffusion Mamba (DiM-3D). This
architecture forgoes traditional attention mechanisms, instead utilizing the
inherent efficiency of the Mamba architecture to maintain linear complexity
with respect to sequence length. DiM-3D is characterized by fast inference
times and substantially lower computational demands, quantified in reduced
Gflops, thereby addressing the key scalability issues of prior models. Our
empirical results on the ShapeNet benchmark demonstrate that DiM-3D achieves
state-of-the-art performance in generating high-fidelity and diverse 3D shapes.
Additionally, DiM-3D shows superior capabilities in tasks like 3D point cloud
completion. This not only proves the model's scalability but also underscores
its efficiency in generating detailed, high-resolution voxels necessary for
advanced 3D shape modeling, particularly excelling in environments requiring
high-resolution voxel sizes. Through these findings, we illustrate the
exceptional scalability and efficiency of the Diffusion Mamba framework in 3D
shape generation, setting a new standard for the field and paving the way for
future explorations in high-resolution 3D modeling technologies.
