UprightNet

PyTorch implementation of paper "UprightNet: Geometry-Aware Camera Orientation Estimation from Single Images", ICCV 2019

Dependency

The code is tested with Python3, Pytorch >= 1.0 and CUDA >= 10.0, the dependencies includes

• tensorboardX
• matplotlib
• opencv
• scikit-image
• scipy

Dataset

• Download pre-processed InteriorNet and ScanNet, as well as their corresponding training/validation/testing txt files from link
• Modify the paths in train.py, test.py and txt files to match the dataset path in your machine.

Training

• To train the network on the InteriorNet, run

	python train.py --mode ResNet --dataset interiornet --w_grad 0.25 --w_pose 2.0

• To train the network on the ScanNet, run

	python train.py --mode ResNet --dataset scannet --w_grad 0.25 --w_pose 0.5

Testing:

• Download checkpoints.zip from link, unzip it and make sure checkpoints folder is in the root directory of codebase.

• To evaluate InteriorNet pretrained network on the InteriorNet testset, run

	python test.py --mode ResNet --dataset interiornet

• To evaluate ScanNet pretrained network on the ScanNet testset, run

	python test.py --mode ResNet --dataset scannet