Log In Sign Up

SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer

by   Haoran Zhou, et al.

Point cloud completion has become increasingly popular among generation tasks of 3D point clouds, as it is a challenging yet indispensable problem to recover the complete shape of a 3D object from its partial observation. In this paper, we propose a novel SeedFormer to improve the ability of detail preservation and recovery in point cloud completion. Unlike previous methods based on a global feature vector, we introduce a new shape representation, namely Patch Seeds, which not only captures general structures from partial inputs but also preserves regional information of local patterns. Then, by integrating seed features into the generation process, we can recover faithful details for complete point clouds in a coarse-to-fine manner. Moreover, we devise an Upsample Transformer by extending the transformer structure into basic operations of point generators, which effectively incorporates spatial and semantic relationships between neighboring points. Qualitative and quantitative evaluations demonstrate that our method outperforms state-of-the-art completion networks on several benchmark datasets. Our code is available at


page 1

page 2

page 3

page 4


SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer

Point cloud completion aims to predict a complete shape in high accuracy...

SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification

Point clouds are often the default choice for many applications as they ...

ShapeFormer: Transformer-based Shape Completion via Sparse Representation

We present ShapeFormer, a transformer-based network that produces a dist...

Point Cloud Completion by Learning Shape Priors

In view of the difficulty in reconstructing object details in point clou...

Voxel-based Network for Shape Completion by Leveraging Edge Generation

Deep learning technique has yielded significant improvements in point cl...

High-Fidelity Point Cloud Completion with Low-Resolution Recovery and Noise-Aware Upsampling

Completing an unordered partial point cloud is a challenging task. Exist...

AGConv: Adaptive Graph Convolution on 3D Point Clouds

Convolution on 3D point clouds is widely researched yet far from perfect...