How to Rethink Inductive Biases for Surface Normal Estimation

If you are keen on enhancing your surface normal estimation skills, the official implementation of the paper **Rethinking Inductive Biases for Surface Normal Estimation** presented at CVPR 2024 could be the breakthrough you’ve been waiting for. In this guide, we will walk you through the installation and setup of the DSINE model using a step-by-step approach. So, let’s dive in!

Why Rethink Inductive Biases?

Surface normal estimation is crucial in fields like computer vision and 3D modeling, where understanding surface orientation appears indispensable. However, existing methods often fail because they don’t incorporate necessary inductive biases. This paper proposes a methodology to improve estimations by:

• Utilizing per-pixel ray direction.
• Encoding the relationship between neighboring normals through relative rotation.

The result? Crisp and smooth predictions for images of arbitrary resolution!

Getting Started: Step-by-Step Instructions

We will proceed through four steps. If you only want to test the model, you can stop after Step 1.

Step 1: Test DSINE on Some Images

This step requires minimal dependencies:

conda create --name DSINE python=3.10
conda activate DSINE
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
python -m pip install geffnet

Next, download the model weights from this link and save it under projects/dsine/checkpoints. Ensure it maintains the same folder structure as the Google Drive.

Finally, run the following command from the projects/dsine folder:

python test_minimal.py .experiments/exp001_cvpr2024_dsine.txt

This will generate predictions for images saved under projects/dsine/samples/img.

Step 2: Test DSINE on Benchmark Datasets

To proceed with deeper evaluation, follow these instructions:

python -m pip install tensorboard opencv-python matplotlib pyrealsense2 vidgear yt_dlp mss

Then download the evaluation datasets from the same link as above.

• Set DATASET_DIR and EXPERIMENT_DIR in projects/__init__.py.
• To run benchmark performance:

python test.py .experiments/exp001_cvpr2024_dsine.txt --mode benchmark

Step 3: Train DSINE

Want to fine-tune the model? Here’s how:

python train.py .experiments/exp000_test/test.txt

Use tensorboard --logdir EXPERIMENT_DIR/dsine/exp000_test/test/log to visualize the training. To improve results, consider creating a custom data loader and refine data augmentation techniques.

Step 4: Start Your Own Surface Normal Estimation Project

If you’re eager to launch your own project, it’s easy:

• Inspect the projects/baseline_normal directory for different CNN architectures.
• Run:

python train.py .experiments/exp000_test/test.txt

Troubleshooting

If you encounter issues during installation or execution:

• Ensure all dependencies are installed correctly.
• If you receive model weight errors, double-check the folder structure.
• For performance issues, experiment with different dataset splits and augmentation techniques.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Analogy: Understanding the Code and Inductive Biases

Think of surface normal estimation like a skilled craftsman trying to carve a statue from a block of stone. The craftsman must understand not just the block of stone (the image) but also how the light interacts with the surface (the ray direction) and how the stone’s texture changes (the relationship with neighboring normals). By mastering these tools and their nuances, the craftsman ensures that the final statue is not just lifeless rock but a beautiful representation that captures the essence of the original block. Just like that craftsman, the DSINE implements specific techniques to accurately estimate surface normals, thereby producing high-quality outputs.