MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation (Technical Report)

Introduction

This is the PyTorch implementation for our technical report which achieves the state-of-the-art performance on the 3D instance segmentation task of the ScanNet benchmark.

Installation

pip install -r requirements.txt

Data preparation

To prepare training data from ScanNet mesh models, please run:

python train.py --task=prepare --dataFolder=[SCANNET_PATH] ----labelFile=[SCANNET_LABEL_FILE_PATH (i.e., scannetv2-labels.combined.tsv)]

Training

To train the main model which predict semantics and affinities, please run:

python train.py --restore=0 --dataFolder=[SCANNET_PATH]

Validation

To validate the trained model, please run:

python train.py --restore=1 --dataFolder=[SCANNET_PATH] --task=test

Inference

To run the inference using the trained model, please run:

python inference.py --dataFolder=[SCANNET_PATH] --task=predict_cluster split=val

The task option indicates:

• "predict": predict semantics and affinities
• "cluster": run the clustering algorithm based on the predicted affinities
• "write": write instance segmentation results

The "task" option can contain any combinations of these three tasks, but the earlier task must be run before later tasks. And a task only needs to be run once. The "split" option specifies the data split to run the inference.

Write results for the final evaluation

To train the instance confidence model, please first generate the instance segmentation results:

python inference.py --dataFolder=[SCANNET_PATH] --task=predict_cluster split=val
python inference.py --dataFolder=[SCANNET_PATH] --task=predict_cluster split=train

Then train the confidence model:

python train.py --restore=0 --dataFolder=[SCANNET_PATH]

Predict instance confidence, add additional instances for certain semantic labels, and write instance segmentation results:

python inference.py --dataFolder=[SCANNET_PATH] --task=predict_cluster_write split=test