update

main.bbl

@@ -69,6 +69,11 @@ C.~Chen, J.~Liu, C.~Zhou, J.~Tang, and G.~Wu, ``Sketch and refine: Towards fast
   and accurate lane detection,'' in \emph{Proceedings of the AAAI Conference on
   Artificial Intelligence}, vol.~38, no.~2, 2024, pp. 1001--1009.
 
+\bibitem{clrernet}
+H.~Honda and Y.~Uchida, ``Clrernet: improving confidence of lane detection with
+  laneiou,'' in \emph{Proceedings of the IEEE/CVF Winter Conference on
+  Applications of Computer Vision}, 2024, pp. 1176--1185.
+
 \bibitem{tusimple}
 \BIBentryALTinterwordspacing
 {TuSimple}, ``Tusimple benchmark,'' 2020, accessed: September 2020. [Online].
@@ -156,11 +161,6 @@ J.~Liu, Z.~Zhang, M.~Lu, H.~Wei, D.~Li, Y.~Xie, J.~Peng, L.~Tian, A.~Sirasao,
   and E.~Barsoum, ``Sparse laneformer,'' \emph{arXiv preprint
   arXiv:2404.07821}, 2024.
 
-\bibitem{clrernet}
-H.~Honda and Y.~Uchida, ``Clrernet: improving confidence of lane detection with
-  laneiou,'' in \emph{Proceedings of the IEEE/CVF Winter Conference on
-  Applications of Computer Vision}, 2024, pp. 1176--1185.
-
 \bibitem{detr}
 N.~Carion, F.~Massa, G.~Synnaeve, N.~Usunier, A.~Kirillov, and S.~Zagoruyko,
   ``End-to-end object detection with transformers,'' in \emph{European
@@ -261,6 +261,12 @@ H.~Abualsaud, S.~Liu, D.~B. Lu, K.~Situ, A.~Rangesh, and M.~M. Trivedi,
 H.~Chen, M.~Wang, and Y.~Liu, ``Bsnet: Lane detection via draw b-spline curves
   nearby,'' \emph{arXiv preprint arXiv:2301.06910}, 2023.
 
+\bibitem{eigenlanes}
+D.~Jin, W.~Park, S.-G. Jeong, H.~Kwon, and C.-S. Kim, ``Eigenlanes: Data-driven
+  lane descriptors for structurally diverse lanes,'' in \emph{Proceedings of
+  the IEEE/CVF conference on computer vision and pattern recognition}, 2022,
+  pp. 17\,163--17\,171.
+
 \bibitem{enetsad}
 Y.~Hou, Z.~Ma, C.~Liu, and C.~C. Loy, ``Learning lightweight lane detection
   cnns by self attention distillation,'' in \emph{Proceedings of the IEEE/CVF

main.tex

@@ -132,7 +132,7 @@ Regrading the first issue, \cite{clrnet} introduced learned anchors, where the a
 
 Regarding the second issue, nearly all anchor-based methods (including those mentioned above) require direct or indirect Non-Maximum Suppression (NMS) post-processing to eliminate redundant predictions. Although it is necessary to eliminate redundant predictions, NMS remains a suboptimal solution. On the one hand, NMS is not deployment-friendly because it involves defining and calculating distances (e.g., Intersection over Union) between lane pairs. This is more challenging than bounding boxes in general object detection due to the complexity of lane geometry. On the other hand, NMS fails in some dense scenarios where the lane ground truths are closer together compared to sparse scenarios. A larger distance threshold may result in false negatives, as some true positive predictions might be eliminated (as shown in Fig. \ref{nms setting} (a) and (b)) by mistake. Conversely, a smaller distance threshold may not eliminate redundant predictions effectively and can leave false positives (as shown in Fig. \ref{nms setting} (c) and (d)). Achieving an optimal trade-off in all scenarios by manually setting the distance threshold is challenging. The root cause of this problem is that the distance definition in NMS considers only geometric parameters while ignoring the semantic context in the image. Thus, when two predictions are “close” to each other, it is nearly impossible to determine whether one of them is redundant.
 
-To address the two issues outlined above, we propose PolarRCNN, a novel anchor-based method for lane detection. For the first issue, we introduce local and global heads based on the polar coordinate system to create anchors with more accurate locations and reduce the number of proposed anchors in sparse scenarios, as illustrated in Fig. \ref{anchor setting} (c). Compared to state-of-the-art previous work \cite{clrnet} which uses 192 anchors, PolarRCNN employs only 20 anchors to cover potential lane ground truths. For the second issue, we have revised FastNMS to Graph-based FastNMS and introduced a new heuristic graph neural network block (Polar GNN block) integrated into the non-maximum suppression (NMS) head. The Polar GNN block offers a more interpretable structure compared to traditional NMS, achieving nearly equivalent performance in sparse scenarios and superior performance in dense scenarios. We conducted experiments on five major benchmarks: TuSimple \cite{tusimple}, CULane \cite{scnn}, LLAMAS \cite{llamas}, CurveLanes \cite{curvelanes}, and DL-Rail \cite{dalnet}. Our proposed method demonstrates competitive performance compared to state-of-the-art methods.
+To address the two issues outlined above, we propose PolarRCNN, a novel anchor-based method for lane detection. For the first issue, we introduce local and global heads based on the polar coordinate system to create anchors with more accurate locations and reduce the number of proposed anchors in sparse scenarios, as illustrated in Fig. \ref{anchor setting} (c). Compared to state-of-the-art previous work \cite{clrnet}\cite{clrernet} which uses 192 anchors, PolarRCNN employs only 20 anchors to cover potential lane ground truths. For the second issue, we have revised FastNMS to Graph-based FastNMS and introduced a new heuristic graph neural network block (Polar GNN block) integrated into the non-maximum suppression (NMS) head. The Polar GNN block offers a more interpretable structure compared to traditional NMS, achieving nearly equivalent performance in sparse scenarios and superior performance in dense scenarios. We conducted experiments on five major benchmarks: TuSimple \cite{tusimple}, CULane \cite{scnn}, LLAMAS \cite{llamas}, CurveLanes \cite{curvelanes}, and DL-Rail \cite{dalnet}. Our proposed method demonstrates competitive performance compared to state-of-the-art methods.
 
 Our main contributions are summarized as follows:
 
@@ -637,11 +637,11 @@ All input images are cropped and resized to $800\times320$. Similar to \cite{clr
         \cline{1-1}
         BézierLaneNet\cite{bezierlanenet} &ResNet18&73.67&-&90.22&71.55&62.49&70.91&45.30&84.09&58.98&\textbf{996} &68.70\\
         BSNet\cite{bsnet}         &DLA34   &80.28&-&93.87&78.92&75.02&82.52&54.84&90.73&74.71&1485&75.59\\
-        Eigenlanes\cite{enginlanes}    &ResNet50&77.20&-&91.7 &76.0 &69.8 &74.1 &52.2 &87.7 &62.9 &1509&71.8 \\
+        Eigenlanes\cite{eigenlanes}    &ResNet50&77.20&-&91.7 &76.0 &69.8 &74.1 &52.2 &87.7 &62.9 &1509&71.8 \\
         \cline{1-1}
         \textbf{Keypoint} \\
         \cline{1-1}
-        CurveLanes-NAS-L\cite{curvelanes} &-u        &74.80&-&90.70&72.30&67.70&70.10&49.40&85.80&68.40&1746&68.90\\
+        CurveLanes-NAS-L\cite{curvelanes} &-  &74.80&-&90.70&72.30&67.70&70.10&49.40&85.80&68.40&1746&68.90\\
         FOLOLane\cite{fololane}         &ResNet18 &78.80&-&92.70&77.80&75.20&79.30&52.10&89.00&69.40&1569&74.50\\
         GANet-L\cite{ganet}          &ResNet101&79.63&-&93.67&78.66&71.82&78.32&53.38&89.86&77.37&1352&73.85\\
         \cline{1-1}

make.sh

@@ -1,6 +1,6 @@
 # latexmk -c
-latexmk -pvc -xelatex -interaction=nonstopmode main.tex
-# latexmk -quiet -interaction=nonstopmode --pvc --pdf main.tex
+# latexmk -pvc -xelatex -interaction=nonstopmode main.tex
+latexmk -quiet -interaction=nonstopmode --pvc --pdf main.tex
 # latexmk -pdf -interaction=nonstopmode -pvc main.tex