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ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer

Created by Shanshan Li, Pan Gao*, Xiaoyang Tan, Mingqiang Wei

This repository contains PyTorch implementation for ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer (Accepted by CVPR 2023).

Problems such as equipment defects or limited viewpoints will lead the captured point clouds to be incomplete. Therefore, recovering the complete point clouds from the partial ones plays an vital role in many practical tasks, and one of the keys lies in the prediction of the missing part. In this paper, we propose a novel point cloud completion approach namely ProxyFormer that divides point clouds into existing (input) and missing (to be predicted) parts and each part communicates information through its proxies. Specifically, we fuse information into point proxy via feature and position extractor, and generate features for missing point proxies from the features of existing point proxies. Then, in order to better perceive the position of missing points, we design a missing part sensitive transformer, which converts random normal distribution into reasonable position information, and uses proxy alignment to refine the missing proxies. It makes the predicted point proxies more sensitive to the features and positions of the missing part, and thus make these proxies more suitable for subsequent coarse-to-fine processes. Experimental results show that our method outperforms state-of-the-art completion networks on several benchmark datasets and has the fastest inference speed.

intro

🔥News

2023-02-28 ProxyFormer is accepted by CVPR 2023.
2022-11-05 ProxyFormer outperforms state-of-the-art completion networks on several benchmark datasets and has the fastest inference speed (PCN: CD 6.77 & DCD 0.577 etc.).

Usage

Requirements

PyTorch >= 1.9.0
python >= 3.7
CUDA >= 11.0
GCC >= 4.9
torchvision
open3d
plyfile
tensorboardX

pip install -r requirements.txt

Building Pytorch Extensions for Chamfer Distance, PointNet++ and kNN

NOTE: PyTorch >= 1.9 and GCC >= 4.9 are required.

# Using a bash to install them
bash install.sh

The solution for a common bug in chamfer distance installation can be found in Issue [#6] of PoinTr (https://github.com/yuxumin/PoinTr/issues/6)

Dataset

The details of ShapeNet-55/34 datasets and other existing datasets can be found in DATASET.md.

Evaluation

To evaluate a pre-trained ProxyFormer model on the Three Dataset with single GPU, run:

bash ./scripts/test.sh <GPU_IDS>  \
    --ckpts <path> \
    --config <config> \
    --exp_name <name> \
    [--mode <easy/median/hard>]

Some examples:

Test the ProxyFormer pretrained model on the PCN benchmark:

bash ./scripts/test.sh 0 \
    --ckpts ./pretrained/ckpt-best.pth \
    --config ./cfgs/PCN_models/our_model.yaml \
    --exp_name example

Test the ProxyFormer pretrained model on ShapeNet55 benchmark (easy mode):

bash ./scripts/test.sh 0 \
    --ckpts ./pretrained/ckpt-best.pth \
    --config ./cfgs/ShapeNet55_models/our_model.yaml \
    --mode easy \
    --exp_name example

Training

To train ProxyFormer from scratch, run:

# Use DistributedDataParallel (DDP)
bash ./scripts/dist_train.sh <NUM_GPU> <port> \
    --config <config> \
    --exp_name <name> \
    [--resume] \
    [--start_ckpts <path>] \
    [--val_freq <int>]
# or just use DataParallel (DP)
bash ./scripts/train.sh <GPUIDS> \
    --config <config> \
    --exp_name <name> \
    [--resume] \
    [--start_ckpts <path>] \
    [--val_freq <int>]

Some examples:

Train a ProxyFormer model with a single GPU:

bash ./scripts/train.sh 0 \
    --config ./cfgs/PCN_models/our_model.yaml \
    --exp_name example

Completion Results on PCN and ShapeNet-55

PCN

results

ShapeNet-55

results results

Acknowledgements

Our code is inspired by PoinTr and SeedFormer. Thanks for their excellent works.

Citation

If you find our work useful in your research, please consider citing: