Rethinking Inductive Biases for Surface Normal Estimation

Official implementation of the paper

Rethinking Inductive Biases for Surface Normal Estimation

CVPR 2024 [oral]

Gwangbin Bae and Andrew J. Davison

[paper.pdf] [arXiv] [youtube] [project page]

Abstract

Despite the growing demand for accurate surface normal estimation models, existing methods use general-purpose dense prediction models, adopting the same inductive biases as other tasks. In this paper, we discuss the inductive biases needed for surface normal estimation and propose to (1) utilize the per-pixel ray direction and (2) encode the relationship between neighboring surface normals by learning their relative rotation. The proposed method can generate crisp — yet, piecewise smooth — predictions for challenging in-the-wild images of arbitrary resolution and aspect ratio. Compared to a recent ViT-based state-of-the-art model, our method shows a stronger generalization ability, despite being trained on an orders of magnitude smaller dataset.

Getting Started

Start by installing the dependencies.

conda create --name DSINE python=3.10
conda activate DSINE

conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
python -m pip install geffnet
python -m pip install glob2

Then, download the model weights from this link and save it under ./checkpoints/.

Test on images

• Run python test.py to generate predictions for the images under ./samples/img/. The result will be saved under ./samples/output/.
• Our model assumes known camera intrinsics, but providing approximate intrinsics still gives good results. For some images in ./samples/img/, the corresponding camera intrinsics (fx, fy, cx, cy - assuming perspective camera with no distortion) is provided as a .txt file. If such a file does not exist, the intrinsics will be approximated, by assuming $60^\circ$ field-of-view.

Additional instructions

If you want to make contributions to this repo, please make a pull request and add instructions in the following format.

Using torch hub to predict normal (contribution by hugoycj)

import torch
import cv2
import numpy as np

# Load the normal predictor model from torch hub
normal_predictor = torch.hub.load("hugoycj/DSINE-hub", "DSINE", trust_repo=True)

# Load the input image using OpenCV
image = cv2.imread(args.input, cv2.IMREAD_COLOR)
h, w = image.shape[:2]

# Use the model to infer the normal map from the input image
with torch.inference_mode():
    normal = normal_predictor.infer_cv2(image)[0]  # Output shape: (H, W, 3)
    normal = (normal + 1) / 2  # Convert values to the range [0, 1]

# Convert the normal map to a displayable format
normal = (normal * 255).cpu().numpy().astype(np.uint8).transpose(1, 2, 0)
normal = cv2.cvtColor(normal, cv2.COLOR_RGB2BGR)

# Save the output normal map to a file
cv2.imwrite(args.output, normal)

If the network is unavailable to retrieve weights, you can use local weights for torch hub as shown below:

normal_predictor = torch.hub.load("hugoycj/DSINE-hub", "DSINE", local_file_path='./checkpoints/dsine.pt', trust_repo=True)

Generating ground truth surface normals

We provide the code used to generate the ground truth surface normals from ground truth depth maps. See data/d2n/README.md for more detail.

About the coordinate system

We use the right-handed coordinate system with (X, Y, Z) = (right, down, front). An important thing to note is that both the ground truth normals and our prediction are the outward normals. For example, in the case of a fronto-parallel wall facing the camera, the normals would be (0, 0, 1), not (0, 0, -1). If you instead need to use the inward normals, please do normals = -normals.

Citation

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

@inproceedings{bae2024dsine,
    title={Rethinking Inductive Biases for Surface Normal Estimation},
    author={Gwangbin Bae and Andrew J. Davison},
    booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2024}
}