Welcome to your go-to guide for using the DoubleTake method for depth estimation! This tutorial will provide you with the steps to set up and execute the project using PyTorch. We’ll ensure that you navigate through the complexities with ease and confidence. Let’s jump right in!

Overview

DoubleTake takes as input posed RGB images and outputs a depth map for a target image. It builds a mesh, which is used to improve depth estimates either online (incrementally) or offline (after a full pass). This innovative approach provides robust depth estimation that’s particularly effective for various datasets.

Setup

Before getting started, we must install some necessary tools. Follow these easy steps to set up your environment:

• Install Mamba (package manager):

make install-mamba

• Create a new Mamba environment called doubletake:

make create-mamba-env

• Activate the newly created environment:

mamba activate doubletake

• In the code directory, install the repo as a pip package:

pip install -e .

• If prompted, run:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib

Understanding the Code through Analogy

Think of using DoubleTake like preparing an elaborate recipe. You gather all the ingredients (data) and create a perfect setup (environment) for cooking (training a model that processes your images). The mesh is like an advanced cooking pan that allows you to cook everything perfectly – it helps combine different elements into a cohesive dish (depth map). Just as you sometimes tweak the ingredients based on taste (fine-tuning your model), the mesh adapts to improve depth predictions at each step.

Running Out of the Box!

If you’re eager to see results right away, we’ve included two scans for immediate testing:

• Download the weights for the hero_model into the weights directory.
• Download the scans and unzip them into the datasets folder.
• To execute the model, apply the following command:

CUDA_VISIBLE_DEVICES=0 python -m doubletake.test_offline_two_pass --name doubletake_offline --output_base_path $OUTPUT_PATH --config_file configs/models/doubletake_model.yaml --load_weights_from_checkpoint weights/doubletake_model.ckpt --data_config configs/data/vdr/vdr_default_offline.yaml --num_workers 8 --batch_size 2 --fast_cost_volume --run_fusion --depth_fuser custom_open3d --fusion_max_depth 3.5 --fusion_resolution 0.02 --dump_depth_visualization

Testing and Evaluation

You can evaluate your model on the depth benchmark of ScanNetv2 using commands provided in the documentation. Adjust the parameters such as batch size and output paths as necessary:

CUDA_VISIBLE_DEVICES=0 python -m doubletake.test_incremental --name doubletake_incremental --config_file configs/models/doubletake_model.yaml --load_weights_from_checkpoint weights/doubletake_model.ckpt --data_config configs/data/scannet/scannet_default_test.yaml --num_workers 12 --batch_size 1 --fast_cost_volume --output_base_path $OUTPUT_DIR

Troubleshooting

If you encounter issues, here are some common problems and their solutions:

• GLIBCXX_3.4.29 not found error: This error typically indicates a missing library. Ensure you’ve run the command to export the LD_LIBRARY_PATH correctly.
• Installation Errors: Verify that all dependencies are correctly installed. Mamba can help manage this more effectively than pip.
• If the model fails to produce accurate depth maps, ensure that the data inputs are correctly formatted according to the expected directory structure.

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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.