The main goal for the project is to develop a computer vision model that can estimate depth and image extrinsics using a provided dataset. The dataset will have all the information needed to train test and evaluate the model. The model should be able to accurately estimate the depth of objects in an image and determine the extrinsic parameters and other variables.

This will involve several different steps for the learners, including:

• Understanding the provided dataset and the format of the data.

• Preprocessing the dataset to extract relevant features and labels for training the model.

• Developing and training a computer vision model.

• Developing and testing an architecture that will be used for the training of the model.

• Evaluating the performance of the model using appropriate metrics.

• Optimizing the model for better accuracy and efficiency.

• Documenting the entire process and presenting the findings.

By the end of the project, learners should complete the following tasks:

• Preprocessed dataset with extracted features and labels.

• Trained computer vision model for depth estimation and camera extrinsic estimation.

• Evaluation report with performance metrics and analysis.

• Optimized model for improved accuracy and efficiency.

• Documentation of the entire process and a presentation summarizing the findings.

Final deliverables:

• Source code for the developed computer vision model.

• A detailed report outlining the methodology, results, and conclusions.

• A brief presentation summarizing the project and its outcomes.

Access to GPU and other Programming Related Resources.

Access to Large Comprehensive Dataset.

Access to Generative AI models if needed.