3D Mesh Encoder

An Executor that receives Documents containing point sets data in its tensor attribute, with shape (N, 3) and encodes it to embeddings of shape (D,). Now, the following pretrained models are ready to be used to create embeddings:

PointConv-Shapenet-d512: A PointConv model resulted in 512 dimension of embeddings, which is finetuned based on ShapeNet dataset.
PointConv-Shapenet-d1024: A PointConv model resulted in 1024 dimension of embeddings, which is finetuned based on ShapeNet dataset.

Usage

via Docker image (recommended)

from jina import Flow

f = Flow().add(uses='jinahub+docker://MeshDataEncoder', \
               uses_with={'pretrained_model': 'PointConv-Shapenet-d512'})

via source code

from jina import Flow

f = Flow().add(uses='jinahub://MeshDataEncoder', \
               uses_with={'pretrained_model': 'PointConv-Shapenet-d512'})

This Executor offers a GPU tag to speed up encoding. For more information on how to run the executor on GPU, check out the documentation.

How to finetune pretrained-model?

install finetuner

$ pip install finetuner

prepare dataset

TBD...

finetuning model with labeled dataset

$ python finetune.py --help

$ python finetune.py --model_name pointconv \
    --train_dataset /path/to/train.bin \
    --eval_dataset /path/to/eval.bin \
    --batch_size 128 \
    --epochs 50

finetuning model with unlabeled dataset

$ python finetune.py --model_name pointconv \
    --train_dataset /path/to/unlabeled_data.bin \
    --interactive

References

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointConv: Deep Convolutional Networks on 3D Point Clouds

nishchaychawla / executor-3d-encoder