August 2021
tl;dr: Regresses the extrinsics and uses feature transfer to compensate.
This approach correctly addresses one drawbacks from existing mono3D dataset which assumes a fixed extrinsics. This is not true in industry applications due to potholed and uneven roads. The paper introduces a method to relax the constraint of assuming a fixed extrinsics.
Personally I am not super confident about the approach to regress extrinsics with horizon and vanishing point. This may work on highways but in crowded urban scenario this may fail miserably.
- Camera extrinsics regression with detecting vanishing point and horizon change
- Feature transfer by extrinsic parameters
- Intuition: low-level features like edges are closely related to extrinsics (contents), while high-level features like texture and illumination are not related to extrinsics (style). This resembles the image style transfer method.
- Two input images, one original and one disturbed. The disturbed image is restored/aligned by the predicted extrinsics.
- Content loss: between the feature map of the original image and the realigned disturbed image.
- Style loss (Frobenius norm of Gram matrix)
- Summary of technical details
- The mathematical formulation is not easy to follow and I am not sure I fully understand. I may need to revisit one day if necessary.
- We perhaps can do data augmentation to boost the robustness against extrinsics change.
- Code on github to be released as of 08/08/2021.