Challenges In Replicating Middlebury Dataset Results With FoundationStereo

Introduction

This article delves into the challenges encountered while trying to replicate the results of the FoundationStereo model on the Middlebury2014 and ETH3D datasets. As deep learning models become increasingly prevalent in computer vision, the reproducibility of results becomes crucial for validating research and ensuring progress in the field. The discrepancy between the officially reported results and the reproduced results highlights the complexities involved in replicating research findings, especially in stereo matching tasks. This exploration aims to provide insights into potential factors contributing to these differences and offer guidance for those facing similar challenges. Understanding these challenges is vital for researchers and practitioners alike, as it helps in refining methodologies, debugging implementations, and fostering a more robust and transparent research environment. The ability to accurately reproduce results is a cornerstone of the scientific method, and addressing the issues outlined here is essential for advancing the state-of-the-art in stereo vision and related fields.

Background on FoundationStereo and Datasets

FoundationStereo is a notable model in the field of stereo matching, designed to estimate depth or disparity maps from stereo image pairs. Stereo matching is a fundamental problem in computer vision, with applications ranging from 3D reconstruction to autonomous navigation. The model's architecture and training process are crucial factors in its performance, and any subtle differences in implementation or environment can lead to variations in results. The Middlebury2014 dataset is a widely used benchmark for stereo algorithms, known for its diverse set of scenes and challenging imaging conditions. It includes both training and test sets, with varying levels of difficulty in terms of texture, illumination, and occlusions. The ETH3D dataset is another popular benchmark, providing high-resolution stereo images captured in real-world environments. It is particularly valuable for assessing the generalization capability of stereo models. When evaluating stereo matching models, several metrics are commonly used, including Bad Pixel percentages (BP-0.5, BP-1, BP-2) and End-Point-Error (EPE). These metrics quantify the accuracy of the predicted disparity maps by measuring the percentage of pixels with errors exceeding certain thresholds and the average disparity error, respectively. Achieving satisfactory performance on these datasets requires careful attention to detail in both model implementation and evaluation procedures.

The Discrepancy in Results

The core issue lies in the significant difference between the officially reported results for the FoundationStereo model and the results obtained during the replication attempt. The table below summarizes the performance metrics on the Middlebury2014 (F and Q, training set) and ETH3D (test set) datasets:

As the data clearly indicates, the reproduction results for Middlebury2014-F are substantially worse than the official results, with BP-0.5 jumping from 9.46 to 35.1 and EPE increasing dramatically from 0.33 to 8.43. While the discrepancy on ETH3D is less severe, there is still a notable degradation in performance. These differences necessitate a thorough investigation into the potential causes, which could range from implementation errors to environmental factors.

The stark contrast in performance metrics underscores the challenges in replicating research findings, particularly in complex deep learning models. A high BP-0.5 score in the reproduction indicates a significant proportion of pixels with disparity errors exceeding 0.5 pixels, suggesting substantial inaccuracies in the disparity estimation. Similarly, the elevated EPE value signals a higher average disparity error across the image, further emphasizing the performance gap. The goal now is to systematically explore possible reasons for these disparities, ensuring a rigorous and transparent approach to reproducing research results.

Potential Factors Contributing to the Discrepancies

Several factors could contribute to the discrepancies observed between the official and reproduced results. These can be broadly categorized into implementation details, data preprocessing, evaluation procedures, and environmental factors.

Implementation Details

• Subtle Bugs or Errors: Even minor bugs in the implementation of the model architecture or training pipeline can significantly impact performance. This includes mistakes in the forward pass, loss function calculation, or optimization process.
• Framework and Library Versions: Differences in the versions of deep learning frameworks (e.g., PyTorch, TensorFlow) and supporting libraries (e.g., CUDA, cuDNN) can lead to variations in numerical precision and computational behavior.
• Parameter Initialization: The initial weights of the neural network can affect the convergence and final performance of the model. Discrepancies in how these weights are initialized might lead to different outcomes.
• Optimization Settings: Subtle differences in the optimization algorithm (e.g., Adam, SGD), learning rate, batch size, and other hyperparameters can influence the training process and the model's ability to generalize.

Data Preprocessing

• Image Resizing: The resizing of input images using cv2.resize with a scaling factor can introduce interpolation artifacts that affect disparity estimation. Differences in resizing methods or parameters can lead to variations in results.
• Input Padding: The use of InputPadder to pad images to specific dimensions (divisible by 32) is crucial for certain network architectures. Incorrect padding or unpadding can result in errors in disparity calculation.
• Data Normalization: The way input images are normalized (e.g., scaling pixel values to a specific range) can influence the model's performance. Inconsistencies in normalization methods can contribute to result discrepancies.
• PFM Conversion: The conversion of disparity maps to the PFM format using disp2pfm.cpp is a critical step in the evaluation process. Issues in this conversion, such as incorrect scaling or handling of invalid disparities, can lead to inaccurate performance metrics.

Evaluation Procedures

• Evaluation Metrics: Discrepancies in how the evaluation metrics (BP-0.5, BP-1, BP-2, EPE) are calculated can lead to different results. Using slightly different implementations of these metrics can introduce variations.
• Masking and Thresholding: The handling of invalid or occluded regions in the images through masking and thresholding can affect the evaluation. Differences in these procedures can impact the reported metrics.
• Submission Format: Incorrect formatting of the submission files for evaluation on platforms like Middlebury can result in parsing errors and inaccurate results.

Environmental Factors

• Hardware: The specific hardware used for training and inference (e.g., GPU model, CPU) can impact performance due to variations in computational capabilities and numerical precision.
• Software Environment: Differences in operating systems, driver versions, and installed software packages can introduce inconsistencies in the execution environment.
• Random Seeds: The use of different random seeds for initialization and data shuffling can lead to variations in training and evaluation results.

Analysis of the Provided Code and Scripts

The provided code snippets and scripts offer valuable insights into the implementation details and potential sources of error. Let's analyze the key components:

Inference Code

The Python script evaluate_model.py implements the inference pipeline for the FoundationStereo model. Here are some critical observations:

• Model Loading: The script loads a pre-trained model from a specified checkpoint directory (args.ckpt_dir). Ensuring that the correct model checkpoint is loaded is crucial for replication.
• Data Loading and Preprocessing: The script reads images using imageio.v2.imread and resizes them using cv2.resize. The resizing operation is a potential source of error if the interpolation method or parameters differ from the original implementation.
• Input Padding: The InputPadder class is used to pad the input images, which is essential for maintaining compatibility with the model's architecture. However, any inconsistencies in the padding or unpadding process can lead to issues.
• Inference Loop: The script iterates through the images in the specified directories and performs inference using the loaded model. The torch.amp.autocast context manager is used for mixed-precision inference, which can improve performance but may also introduce numerical differences.
• Disparity Map Saving: The predicted disparity maps are saved as PNG images using cv2.imwrite and then converted to PFM format using the disp2pfm.cpp tool. The conversion process is a potential bottleneck, and any errors in scaling or format conversion can affect the final evaluation metrics.
• ETH3D Evaluation: The script includes commented-out code for evaluating on the ETH3D dataset. This suggests that the evaluation process for ETH3D might not be fully implemented or tested, which could contribute to the observed discrepancies.

disp2pfm.cpp

The C++ code disp2pfm.cpp converts grayscale disparity images to the PFM format. Key aspects of this code include:

• Image Reading and Writing: The code uses the imageLib.h library to read and write image files. Ensuring that this library is correctly installed and configured is crucial.
• Disparity Scaling: The code scales the disparity values by a factor (dispfact) and converts zero values to infinity if mapzero is enabled. Incorrect scaling or handling of zero values can lead to significant errors in the disparity maps.
• Floating-Point Conversion: The core functionality of the code involves converting byte images to floating-point images. Any numerical precision issues in this conversion can affect the accuracy of the disparity maps.

Debugging Steps and Recommendations

To address the discrepancies, a systematic debugging approach is essential. Here are some recommended steps:

1. Verify Data Preprocessing:
   • Double-check the image resizing and padding procedures to ensure they match the original implementation.
   • Inspect the input images and disparity maps visually to identify any obvious artifacts or errors.
   • Confirm that the data normalization method is consistent with the original model's requirements.
2. Check Model Implementation:
   • Carefully review the model architecture and forward pass to identify any potential bugs or inconsistencies.
   • Compare the loss function calculation and optimization process with the original paper or implementation details.
   • Ensure that the correct parameter initialization method is used.
3. Validate Evaluation Procedures:
   • Implement the evaluation metrics (BP-0.5, BP-1, BP-2, EPE) independently and verify their correctness.
   • Check the masking and thresholding procedures to ensure they align with the evaluation protocol.
   • Confirm that the submission format is correct for the evaluation platform.
4. Isolate Environmental Factors:
   • Use the same deep learning framework and library versions as the original implementation.
   • Run the code on the same hardware configuration if possible.
   • Set the random seeds to the same values as the original experiment to ensure reproducibility.
5. Debug PFM Conversion:
   • Verify that the disp2pfm.cpp tool is correctly configured and compiled.
   • Inspect the PFM files generated by the tool to identify any scaling or format errors.
   • Check the handling of invalid disparities (e.g., zero values) in the conversion process.
6. Stepwise Testing:
   • Test the model on a small subset of the data to identify issues early in the process.
   • Compare intermediate results (e.g., disparity maps before and after PFM conversion) with the expected values.
   • Isolate and test individual components of the pipeline to pinpoint the source of the errors.

Specific Issues and Solutions for the Provided Code

Based on the provided code, here are some specific issues and potential solutions:

1. Image Resizing: The use of cv2.resize with default interpolation may not be optimal. Consider using a more precise interpolation method (e.g., cv2.INTER_LANCZOS4) and ensure that the resizing parameters match the original implementation.
2. Mixed-Precision Inference: The torch.amp.autocast context manager can improve performance but might introduce numerical differences. Try running inference without mixed precision to see if it affects the results.
3. PFM Conversion: The disp2pfm.cpp tool is a critical component. Verify that it is correctly compiled and that the scaling and mapzero parameters are set appropriately. Inspect the generated PFM files for any errors.
4. ETH3D Evaluation: The commented-out code for ETH3D evaluation suggests that this part of the pipeline might not be fully tested. Ensure that the evaluation procedure for ETH3D is correctly implemented and that the results are consistent with the official metrics.
5. File Paths: The file paths specified in the arguments (e.g., --middlebury_data_dir, --middlebury_out_dir) should be carefully checked to ensure they point to the correct directories and files.

Conclusion

Replicating research results, especially in the field of deep learning, is a complex undertaking that requires meticulous attention to detail. The discrepancies observed in the reproduction of the FoundationStereo model on the Middlebury2014 and ETH3D datasets highlight the challenges involved. By systematically analyzing potential factors such as implementation details, data preprocessing, evaluation procedures, and environmental factors, it is possible to identify and address the sources of error. The debugging steps and recommendations provided in this article offer a practical guide for researchers and practitioners facing similar issues. Ultimately, fostering a culture of rigorous reproducibility is essential for advancing the field of computer vision and ensuring the reliability of research findings. By addressing these challenges, we can enhance the credibility and impact of our work, contributing to a more robust and transparent research ecosystem.