Paper: Grouplane: End-to-End 3D Lane Detection with Channel-Wise Grouping (ICLR-2024)

Reviewer comment (Experiments):
Simply dividing each group into N outputs limited the max output lane number of the model.

Author response:
Firstly, it is important to note that the number of lanes on real roads is typically limited. In most cases, there are at most tens of lanes on a road. Therefore, having an upper bound on the prediction number does not significantly impact the practical deployment of GroupLane in real-world applications.

   Secondly, it is worth mentioning that almost all current lane detectors also have an upper bound on the prediction number. For example, the maximum prediction number of DETR is determined by the number of queries, while anchor-based detectors have a maximum prediction number based on the total number of anchors.

   In comparison to other algorithms, GroupLane offers several advantages, including being more lightweight, faster, and demonstrating better precision. Consequently, it is relatively straightforward to increase the maximum prediction number of GroupLane beyond that of other detectors if necessary.

   Considering the aforementioned points, we firmly believe that having a maximum prediction number does not undermine the superiority of GroupLane. The model's performance and efficiency, combined with the practical limitations of lane numbers on real roads, make GroupLane a highly effective solution for lane detection tasks.

Reviewer comment (Reproducibility):
SOM strategy is simple yet effect, details are not well explained or even missing, eg. the matching cost definition.

Author response:
We apologize for the oversight in not providing sufficient details on the SOM strategy in the paper. We have provided a more comprehensive explanation of the matching cost definition and other relevant details in Appendix Section A.4 of the paper, which has been highlighted in cyan. We do not present it here because it is too long and the reply box has a character limit. We hope the reply can address the concern of the Reviewer well.

Reviewer comment (Reproducibility):
It is unclear what will happen when both the vertical and horizontal heads match the ground truth (GT). The paper does not provide a clear explanation or analysis of this scenario.

Author response:
It seems that there are some misunderstanding about our method. In fact, the single-win one-to-one matching strategy employed in our approach explicitly matches one GT with only one prediction from the vertical and horizontal heads (details can refer to Section 3.3 in the paper). This ensures that the scenario where both heads match the same GT does not exist in our framework.

Reviewer comment (Evaluation):
The paper does not address how to ensure stable predictions. It raises concerns about the possibility of different heads predicting the same lane at different time and one of them is not the optimal prediction.

Author response:
The issue of unstable predictions is not a concern in GroupLane. The matching process employed in GroupLane is based on the same principles as the DETR algorithm [1]. The one-to-one matching algorithm ensures that only the predictions that are most similar to the ground truth (GT) are matched. Any predictions that do not match any GT are treated as background.

If a matched prediction for a lane is not of sufficient quality, the loss function will penalize the prediction and encourage the model to generate a better one. This mechanism ensures that the algorithm remains stable by continuously refining and improving the predictions.

[1] Carion N, Massa F, Synnaeve G, et al. End-to-end object detection with transformers[C]//European conference on computer vision.  2020: 213-229.

Reviewer comment (Novelty):
The ultra-fast deep lane detection method has already introduced a hybrid anchor-based lane detection that predicts row-and-column anchors corresponding to lanes.

Author response:
**Q1: The ultra-fast deep lane detection method has already introduced a hybrid anchor-based lane
detection that predicts row-and-column anchors corresponding to lanes.**

A1: Please kindly note that we had cited the method of UFLD as a reference (in the 2nd page: Qin et al., 2020). To address you concern, we give a deep comparison here.

* Though UFLD proposes row-wise classification lane detection, its application is limited to 2D lane detection, which is significantly different from that in 3D lane detection.

* To successfully apply the row-wise classification lane detection to 3D lane detection, we have conducted multiple improvements:

 &ensp;&ensp; (1) First of all, the row-wise classification lane detection method is conducted on the BEV plane, instead of the camera plane (adopted by UFLD). This is because we consider the geometry of lanes is simpler in the BEV plane and thus easier to learn as shown in Figure 6 of the paper.

&ensp;&ensp; (2) Secondly, we build two groups of heads, one for horizontal lanes detection and the other for vertical lanes detection. 

&ensp;&ensp; (3) Thirdly, a novel one-to-one matching algorithm named single-win one-to-one matching (SOM) is developed to match predictions with targets during training for computing loss.  
    
&ensp;&ensp; As a result, although UFLD behaves poorly compared with other counterparts in the 2D lane detection domain, our GroupLane achieves strong performance.

&ensp;&ensp; The above discussion has been included in our paper (page 2 and page 3, marked in brown).