Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection

March 2020

tl;dr: A two-stage method for 3D LLD and a new open dataset.

Overall impression

The paper pointed out one major drawback of 3D LaneNet that the top view projection does not align with the IPM image feature in the presence of a non-zero slope. The proposed “virtual top view” projection will align with IPM. This essentially changes the regression target (not the “anchor representation” the paper argues.)

Concretely, an uphill 3D lane with zero yaw will provide parallel target in the real top view (x, y, 0) but will have diverging target in virtual top view ($\bar{x}$, $\bar{y}$, $0$). Similarly a downhill 3D lane will have converging target in virtual top view. This will align with the IPM’ed image features.

In 3D LaneNet there is no guarantee that the projection of prediction matches the image and lacks the 2D-3D consistency. However in Gen-LaneNet, 3D are lifted from 2D and thus this consistency is inherent to the pipeline.

The paper also decoupled 2D lane line prediction and lifting 2D to 3D. Decoupling 3D structure prediction from 2D LLD: motivated by the fact that encoding of 3D geometry is rather independent from image features. This decoupling makes the module more flexible and scalable. The second stage can be trained with a synthetic dataset alone. The deliberate removal of image feature in the second stage is similar to predicting distance with bbox info only in Object frontal distance, and with skeleton only MonoLoco. A similar stragegy to alleviate sim2real domain gap can be also found in fishing net.

This paper is quite hard to read (like other papers by Baidu, unfortunately), but the underlying ideas are solid. I wished the paper included a clear definition of what is a (real) top view and a virtual top view.

Key ideas

Technical details