PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention

This work presents a novel autoregressive model, PointGrow, which generates realistic point cloud samples from scratch or conditioned on given semantic contexts. This model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points. It is further augmented with dedicated self-attention modules to capture long-range interpoint dependencies during the generation process.

Data

We provided processed point clouds from 7 categories in ShapeNet, including airplane, car, table, chair, bench, cabinet and lamp. The coordinates of those point clouds, arranged as (z, y, x), range from 0 to 1. They are sorted in the order of z, y and x, and can be downloaded from here.

Unconditional PointGrow

Train

The model is trained per category, change the ShapeNet category id when working on different categories.

Category	Id
Airplane	02691156
Car	02958343
Table	04379243
Chair	03001627
Bench	02828884
Cabinet	02933112
Lamp	03636649

• Run unconditional PointGrow training script for airplane category with SACA-A module:

python train_unconditional.py --cat 02691156 --model unconditional_model_saca_a

Model parameters will be stored under "log/unconditional_model_saca_a/02691156".

• To generate 300 point clouds for airplane category using the pre-trained model:

python generate_unconditional.py --cat 02691156 --model unconditional_model_saca_a --tot_pc 300

The generated point clouds will be stored in the format of numpy array under "res/unconditional_model_saca_a/res_02691156.npy".