Partial Extrinsics For Depth-Anything: A Deep Dive

Nov 14, 2025 by Alex Johnson 51 views

Hey there! Thanks for your interest in Depth-Anything, and I'm thrilled to hear you're getting great results in your tests. Your question about supplying extrinsics for only a subset of input views is a fantastic one, and it touches on a key aspect of how these models work. Let's dive in and explore the possibilities and technicalities. This will include how the model processes the data when only partial extrinsics are provided.

Understanding Extrinsics and Intrinsics in Depth Estimation

To understand the core of your question, we first need to grasp the basics of extrinsics and intrinsics. In the world of computer vision, especially when dealing with 3D reconstruction and depth estimation, these are fundamental concepts. Let's break them down:

Intrinsics: These parameters define the internal characteristics of your camera. Think of them as the camera's personality. They include the focal length (how 'zoomed in' the camera is), the principal point (the center of the image), and any distortion coefficients (correcting for lens imperfections). These parameters are crucial for converting 2D image coordinates into 3D space. The intrinsics remain the same regardless of where the camera is located in the real world.
Extrinsics: These parameters define the external characteristics of your camera. They describe the position and orientation of your camera in the 3D world. This includes the rotation and translation of the camera relative to a reference frame. Extrinsics are what tell the model where the camera was when a particular image was taken. They're essential for aligning multiple views of the same scene, which is often necessary for depth estimation.

When we talk about models like Depth-Anything, both intrinsics and extrinsics play a vital role. The model uses these parameters to understand the geometry of the scene. By knowing where each camera was and how it 'sees' the world, the model can estimate the distance of points in the image from the camera (depth). This is a critical aspect of 3D scene understanding. The number of these parameters needs to be the same as the total views, which is where your question comes in.

The Importance of Camera Pose

The camera pose, determined by the extrinsic parameters, is vital. It tells the model the spatial relationship between different views of the same scene. If you're using multiple cameras or moving a single camera, the extrinsics help the model understand how each image relates to the others. Without accurate extrinsics, the model would struggle to create a coherent 3D representation. The model will not be able to understand the depth. It needs to know the position and orientation of the camera for each view to create an accurate depth map.

Addressing the Question: Partial Extrinsics

Now, let's address your core question: Can we set extrinsics only for a subset of input views? The straightforward answer is, typically, the model expects extrinsics (and intrinsics) for every view. However, there are potential workarounds and considerations.

Identity Matrices as Placeholders

Your idea of using identity matrices for the extrinsics of the views you don't have is a smart one. An identity matrix, in the context of extrinsics, effectively means that the camera is at the origin of the world coordinate system, and its orientation is aligned with the world's axes. Here's how this could work:

For Views with Extrinsics: You provide the actual extrinsic parameters (rotation and translation) for the first and last views (as you suggested).
For Other Views: For the views where you don't have extrinsics, you can provide an identity matrix (for rotation) and a zero vector (for translation). This would indicate that, as far as the model is concerned, these views are all taken from the same point in space and with the same orientation. It is up to you to implement your own logic to determine the identity.

Potential Challenges and Considerations

While using identity matrices is a viable approach, it's not a perfect solution. Here are some challenges you might encounter:

Accuracy: If the views with identity matrices are significantly different from the views with real extrinsics (e.g., they're far apart or have a large rotational difference), the depth estimation might suffer. The model will struggle to reconcile the conflicting information.
Model Behavior: The behavior of the model can vary. Some models are more robust to missing or inaccurate extrinsics than others. You might need to experiment to see how Depth-Anything handles this scenario.
Post-processing: You might need to add some post-processing steps to refine the results. For example, you could apply a global alignment to correct any overall drift or misalignment in the 3D reconstruction.
Data Consistency: Ensure that the images are consistent. If you have a moving camera and are only providing extrinsics for the first and last views, the intermediate frames might appear to 'jump' or 'warp' due to the lack of correct pose information.

Implementing the Solution

Implementation will depend on the specific framework or library you're using with Depth-Anything. You'll typically need to:

Prepare your data: Load your images and, for the first and last views, the corresponding extrinsic parameters. For the other views, create identity matrices and zero vectors as placeholders. You might need to convert the extrinsics into a suitable format (e.g., transformation matrices).
Pass data to the model: Feed your images, intrinsics, and extrinsics (including the identity matrices/zero vectors) into the model. Make sure that the data structures are aligned with what the model expects.
Evaluate results: Analyze the depth maps and 3D reconstructions. If you notice artifacts or inaccuracies, adjust your approach, experiment with different settings, or consider additional post-processing steps.

Optimizing for Partial Extrinsics

To make this work effectively, here are some optimization tips:

Data Preprocessing

Image Alignment: If possible, try to align your input images before feeding them into the model. This can help to reduce errors caused by the lack of extrinsics for some views.
Image Pre-filtering: Apply image pre-filtering techniques to reduce noise and enhance features, which can assist in robust depth estimation.

Model Fine-Tuning

Transfer Learning: If possible, fine-tune the model on a dataset that closely matches your image data and camera setup. This can improve the model's ability to handle the partial extrinsics.
Experimentation: Experiment with different model parameters and configurations to see how they impact the results. There is a chance that modifying the parameters of the model can improve the results.

Additional Considerations and Advanced Techniques

Beyond the basic approach, you can explore some advanced techniques to improve your results:

Bundle Adjustment: If you have some overlapping views, bundle adjustment techniques can refine the camera poses (including the ones you estimated). This can improve the consistency of the 3D reconstruction.
Structure-from-Motion (SfM): Explore SfM techniques to estimate the camera poses from the image data itself. This can be useful if you lack ground truth extrinsics for all views. You might be able to create a sparse point cloud and then use the SfM algorithms to create the camera poses from that sparse cloud.
Depth Map Fusion: After generating depth maps for each view, use a fusion technique to combine them into a single, more complete depth map. This can reduce the impact of errors in individual depth maps.

Conclusion: Navigating Partial Extrinsics

So, can you set extrinsics for only a subset of input views? Yes, it's possible, but it comes with a few caveats. Using identity matrices as placeholders is a reasonable starting point. Remember to manage your expectations, as the accuracy of the depth estimation may be affected. Thorough experimentation and possibly some post-processing or advanced techniques might be necessary to get the best results. Good luck, and keep experimenting!

I hope this detailed explanation is helpful! If you have any more questions or need clarification, feel free to ask.

For Further Reading:

Camera Calibration: This resource provides a more in-depth look into camera intrinsics and extrinsics and how to calculate them.