TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving
Authors: Yanping Fu
Xinyuan Liu
Tianyu Li
Yike Ma
Yucheng Zhang
Feng Dai
Venue: NeurIPS 2025 poster ()
Keywords: Autonomous Driving
Primary area: deep_learning
TL;DR: TopoPoint improves topology reasoning via lane endpoint detection, enhanced point-lane interaction, and geometry-based endpoint refinement.

Abstract: Topology reasoning, which unifies perception and structured reasoning, plays a vital role in understanding intersections for autonomous driving. However, its performance heavily relies on the accuracy of lane detection, particularly at connected lane endpoints. Existing methods often suffer from lane endpoints deviation, leading to incorrect topology construction. To address this issue, we propose TopoPoint, a novel framework that explicitly detects lane endpoints and jointly reasons over endpoints and lanes for robust topology reasoning. During training, we independently initialize point and lane query, and proposed Point-Lane Merge Self-Attention to enhance global context sharing through incorporating geometric distances between points and lanes as an attention mask . We further design Point-Lane Graph Convolutional Network to enable mutual feature aggregation between point and lane query. During inference, we introduce Point-Lane Geometry Matching algorithm that computes distances between detected points and lanes to refine lane endpoints, effectively mitigating endpoint deviation. Extensive experiments on the OpenLane-V2 benchmark demonstrate that TopoPoint achieves state-of-the-art performance in topology reasoning (48.8 on OLS). Additionally, we propose DET$_p$ to evaluate endpoint detection, under which our method significantly outperforms existing approaches (52.6 v.s. 45.2 on DET$_p$). The code is released at https://github.com/Franpin/TopoPoint.

TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving
Yanping Fu F23,3, Xinyuan Liu!?, Tianyu Li Yike Ma', Yucheng Zhang', Feng Dai!"
Institute of Computing Technology. Chinese Academy of Science; University of Chinese Academy of Sciences; "Shanghai AI Lab; *Shanghai Innovation Institute fuyanping33s@ictt.acc
Abstract
Topology reasoning, which unifies perception and structured reasoning, plays vital role in understanding intersections for autonomous driving. However, its performance heavily relies on the accuracy of lane detection, particularly con- nected lane endpoints. Existing methods often suffer from lane endpoints leading to incorrect topology construction. To address this issue, propose TopoPoint, a novel framework that explicitly detects lane endpoints and jointly reasons over endpoints and lanes for robust topology reasoning. During training, we independently initialize point and lane query, and proposed Point-Lane Merge Self-Attention to enhance global context sharing through incorporating geomet- ric distances between points and lanes as attention mask We further design Point-Lane Graph Convolutional Network to enable mutual feature aggregation between point and lane query. During inference, we introduce Point-Lane Ge- ometry Matching algorithm that computes distances between detected points and lanes to refine lane endpoints, effectively mitigating endpoint deviation. Extensive experiments on the OpenLane-V benchmark demonstrate that TopoPoint achieves state-of-the-art performance in topology reasoning (48.8 8 on OLS). Additionally, we propose DET, to evaluate endpoint detection, under which our method significantly outperforms existing approaches (52.6 v.s. 45.2 on DETp). The code is released at https: http::://githbb com com/FraapihnToooPoint
deviation,
1 Introduction
In autonomous driving scenarios, perceiving lane markings and traffic elements on the road surface is critical for understanding complex intersection environments. To enable accurate interpretation of the scene and determine feasible driving directions, is essential infer both lane-lane topology and lane- traffic element topology. With the growing trend of end-to-end autonomous driving systems 2 3 perception and reasoning have become increasingly integrated into unified task, referred to as topology reasoning 4.
( 6. 7 88. This task also plays vital role in high-definition (HD) map learning 10 [I [2] and supports downstream modules such as planning and control.
As a continuation of the lane detection task, topology reasoning task need to uniformly process lanes, traffic elements, and their corresponding topological relationships, so the query- based architecture has become the mainstream solution. In this pipeline, the multiple lanes are encoded and predicted through multiple independent queries, as shown in Figure [a). However, since the lane endpoints are actually attached to lane query and are affected by the supervised learning of multiple lanes, it difficult to ensure that the multiple endpoints of the final prediction can strictly coincide, which is called the endpoint deviation problem. This problem already explored preliminarily as early as in the era of lane detection, e.g., the method STSU[(3 aligns the endpoints by moving the entire lane, while the method [aneGAP[4]] adopts path-wise modeling approach, predicting complete
Cooresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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p 000
Point Head
Lane Decoder
Lane Head
Point-Lane Decoder
Point-1 ane
L
Head
Geometry Matching
Q1
L
Fo
bee
Fiee Aep
lanes with deviated endpoints
lanes with overlaping endpoints
(a) The previous pipeline
(b) Our proposed pipeline
Figure 1: Pipeline Comparison. (a) In the previous pipeline, lanes are predicted independently. which leads obvious endpoint deviation. (b) our proposed pipeline, lane endpoints are explicitly modeled, and lanes with overlapping endpoints are obtained through point-lane geometry matching.
lane paths by merging connected lane pieces. However, due to the suboptimal performance of lane detection, these methods have been replaced. A recent work, TopoLogic[ has once again noticed this problem. It integrates the lane-lane geometric distance and semantic similarity alleviate the interference the endpoint deviation in topology reasoning, instead of rectifying the issue itself. Therefore, lane detection is still inaccurate, which means that the endpoint deviation problem has not been completely resolved.
To address the aforementioned issues, propose TopoPoint, novel framework that introduces explicit endpoint detection and fuses features from both lanes and endpoints to enhance topology reasoning, is illustrated in Figure [b). By reasoning over the topological relationship between endpoints and lanes, TopoPoint effectively mitigates the endpoint deviation problem. To enable point detection and facilitate feature interaction between points and lanes during training, we design the point-lane detector, independently initializing point query and lane query. These queries are supervised at the output by separate objectives for lane detection and endpoint detection. We further propose Point-Lane Merge Self-Attention (PLMSA), and it concatenates point and lane query and leverages geometric distances attention masks enhance global context sharing. To enhance point-lane feature interactions, we introduce the Point-Lane Graph Convolutional Network (PI (PLGCN), and it models the topological relationships between points and lanes by constructing an adjacency matrix. This enables bidirectional message passing between point and lane features through Graph Convolutional Network (66))[16). PLGCN serves key component of our Unified Scene Graph Network. This joint learning process significantly enhances the representation capability of both endpoints, lanes and traffic elements, thereby improving topology reasoning performance. During inference, we propose the Point-Lane Geometry Matching (PLGM) algorithm, and it computes geometric distances between detected endpoints and the start and end points of lanes. This allows us to refine lane endpoints by matching points to lanes based on their geometric proximity, effectively mitigating the endpoint deviation issue. Our contributions are summarized as follows:
We identify that the endpoint eviation issue in current methods stems from the fact that lane endpoints simultaneously supervised by multiple lanes. To tackle this, we propose independently detecting endpoints and Point-Lane Geometry Matching algorithm to refine lane endpoints.
2. We introduce TopoPoint, novel framework designed to enhance topology reasoning by incorpo- rating explicit endpoint detection. Within TopoPoint, point query and lane query exchange global contextual information through the proposed Point-Lane Merge Self-Attention, and their feature interaction is further reinforced by the Point-Lane Graph Convolutional Network.
3. All experiments are conducted the Openl 17 benchmark, where our method outper- forms existing approaches and achieves state-of-the-art performance. addition, We introduce DET, for evaluating endpoint detection, and our method achieves notable improvements.
2 Related Work
2.1 Lane Detection
Lane detection is essential for autonomous driving, providing structural cues for road perception 9 12
10 and motion planning R. Traditional methods typically use semantic segmentation identify lane areas in front-view images, but they often struggle with longerange consistency and occlusions.

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To overcome these limitations, vector-basee approaches model lanes as sparse representations. Recent advances in 3D lane detection have been driven by sparse BEV-based object detectors like DETR3D[8] and PETR I9, which use sparse query and viet geometry to reason directly in 3D space. These ideas have inspired new wave of lane detectors. For instance, CurveFormer(20) represents lanes with 3D line anchors and introduces curve query that encode strong positional priors. Anchor3DLane2II extends Lane2TT(22|'s s line anchor pooling and incorporates both intrinsic and extrinsic camera parameters accurately project 3D anchor points onto front-view feature maps. PersFormer 2 leverages deformable attention to learn the transformation from front-view to BEV space, improving spatial alignment. LATR [24] further refines lane modeling by decomposing it into dynamic point-level and lane-level query, enabling finer topological representation.
2.2 Topology Reasoning
Topology reasoning in autonomous driving aims to interpret road scenes and define drivable routes. STSU[13 encodes lane query for topology prediction by DETR(25) LaneGAP[14] applies shortest path algorithms to transform lane-lane topology into overlapping paths. TopoNet 26 combines Deformable DETR 27] with GNN to aggregate features from connected lanes. TopoML (0)
leverages PETR 191 for lane detection and uses a multi-layer perceptron for topology reasoning. TopoLogic [5] integrates geometric and semantic information by combining lane-lane geometric distance with semantic similarity. TopoFormer [3]] introduces unified traffic scene graph to explicitly model lanes. SMERF 32 improves lane detection by incorporating SDMap an additional input, while LaneSegNet 33 uses Lane Attention to identify lane segments. In our work, We introduce endpoint detection enhance topology reasoning and mitigate endpoint deviation.
Method
3.1 Problem Definition
Given surround-view images captured by multiple cameras mounted on vehicle, the topology
reasoning task includes: 3D lane centerline detection 19 35
36 23
the bird's-eye view (BEV)
space, 2D traffic element detection (37) in the front-view view image, topology reasoning [26 B3 [2] among lane centerlines and topology reasoning between lane centerlines and traffic elements. All lane centerlines are represented by multiple sets of ordered point sequences L {4 g
1, 2...... ]. where n4 is the number of lane centerlines and k is the number of points on the lane centerline. All traffic elements
represented using multiple 2D bounding boxes T = {t, R = 1,2,.... T4 ). where n4 is the number of traffic elements. The lane-lane topology. which encodes the connectivity between lanes, is represented by adjacency matrix Gu. The lane-traffic element topology, capturing the association between lanes and traffic elements, is represented by another adjacency matrix Gjg, In addition, the framework includes point detection and point-lane topology reasoning. A set of candidate points P = {pr E R = 0, 2, .2...7 mp is constructed by de-duplicating all endpoints of lane centerlines, where n, is the number of unique endpoints. The point-lane topology GpJ is created by checking whether the point lies on lane centerline.
3.2 Overview
As illustrated in Figure 2 our proposed TopoPoint framework consists of traffic detector, point-lane detector, geometric attention bias, topology head and point-lane result fusion. We downsample the multi-view by a factor of 0.5, while keeping the front-view at its original resolution. During training, all images passed through ResNet-50 pretrained on ImageNet (39 with FPN[40] extract multi-scale features. These features are then encoded into BEV representations using BevFormerfa encoder. In the traffic detector, front-view features are directly processed by Deformable DETR 27 to produce traffic query Q, In the point-lane detector, point query Qp and lane query Qi interact via Point-Lane Merge Self-Attention, which computes geometric attention bias serving as an attention mask to enhance global information sharing. The resulting queries then perform cross-attention with BEV features. Then Qp and Qi together with Q.. are fed into Unified Scene Graph Network. The topology head computes point-lane topology, lane-lane topology and lane-traffic topology. During inference, predicted points and lanes are fused via Point-Lane Geometry Matching algorithm to refine lane endpoints and effectively mitigate the endpoint deviation problem.
3

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P o.0
Gp
Gu
G
Result
Traffic Head
Point Head
Lane llend
Point-Lanc
Lanc-L Lnne Head
Lame-Truffk Head
Topalogg Matris
Distance Matrix
Q.
4
Q2
Inference
(e) Topology Head
Deformable DETR
Uniffied Seene Graph Network
Ma
FFN
FTN
My
Mu
Frr
Peee
Deformable Attention
Deformable Attention
Geumetry Matching
Constructor
mmup
fmop
Fiee
Rackhone
Point-Lam
Z
Mp
Mu
x
1/2x
Zero
Point-I ane Distance
Lane-I.anc Distance
p s.e i (
from veww
Q:
P 0.0
L
(d) Point-Lane Result Fusion
(a) Traffic Detector & LointtLLane Detector
(b) Geometric Attention Bias
Figure 2: TopoPoint framework. (a) In addition to the traffic elements and lanes, lane endpoints
also explicitly perceived in the detector. (b) The geometric attention bias is also incorporated into the point-lane merge self attention module exchange information. (c) On this basis, the queries used for topology reasoning, and the topology is also used for query enhancement in scene graph network. (d) During inference, point-lane result fusion is applied to eliminate endpoint deviation.
3.3 Traffic Detector
To detect traffic elements in the front-view image, we initialize traffic element query Q+ which interact with multi-scale front-view features Ffe via Deformable DETR to compute cross-attention and produce updated representations Q.. The Q, are then passed through the Traffic Head to predict 2D bounding boxes I", The process is as follows:
Q. DeformableDETR((Q. Ffv)
T = TrafficHeed((.))
where Qi E Rixi Fju &R xd and Í RRN. N₂ denotes the number Qt. denotes the feature dimension, (H Wfv) denotes the size of Fbes-
3.4 Point-Lane Detector
We independently initialize point query Qp and lane query Qt. These queries first interact through Point-Lane Merge Self-Attentio to exchange global information. The updated queries then compute cross-attention with the BEV features, followed by two separate feed-forward networks (FFNs). The resulting Qp and Qt are subsequently fed into Unified Scene Graph Network, where they aggregate features from each other via graph convolution networks (GCNs). The enhanced representations are finally used by the point head and lane head to regress endpoints and lane centerlines, respectively.
Point-Lane Merge Self-Atee Solf-Attention. We first concatenate Qp and Q) along the instance dimension to form 2pt- Qp is then used as the query, key, and value in the self-attention computation. The definition of Qp as follows:
Qp = Concat (Qp, Q:)
where p, E N,xd Qi E Rixd Qpt E N Np denotes the number of Qp, Nr denotes the number Qi, Npt N, + N, and d denotes the feature dimension. To incorporate geometric relationships between points and lanes in the BEV space, compute their pairwise

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Gi
M
6CNr
OCNP
pz
Downsomple
Gpt
Q
000
Q:
PLGCN1
PLOCNN
OCN
M
Q1
4
Q2
Mpt (a) Unified Scene
Mpt
G
My
Graph Network
(b) PLGGN
Figure 3: Module details. Based on geometric attention bias and reasoned topology, lane & point queries are enhanced from the associated traffic elements lanes & points by the unified scene graph network, (b) where the PLGCN is designed for better interaction between lanes and points.
geometric distances based on the predicted points P {pr E R 1.2..... Not and lanes T = (T, E TR & = 2............... ......N, Ni} from the previous decoder layer, where denote the number of points in each lane. These distances then transformed by learnable mapping function fmap to obtain geometric bias matrix Mpp. Mpt and Mu, as follows:
Du
Σ
751 = 1,2,
No. = 1,2 ,7,
(4)
Drt Min
p:
/31. _ 10: 291 i 1,2....NP... 1,0j=,,2,.... .I
(5)
Mpt J map (Drt), Mu Smap (Du)
where ig E I denotes the start point of i, i E R23 denotes the end point i. Du E NNN denote the L1 distance from the start points the end points in L,-1, and Dpt E R denote the minimum LI distance from Pt- to the endpoints of L1 Notably, Smap = E is proposed in TopoLogic 15). À are learnable parameters, and ô is the standard deviation of distance matrix D.
To compute self-attention, we concatenate Mpt, Mu form geometric attention bias, which is added to the attention weights computed from 2pt- The self attention process is described as follows:
QN O Vd
Z MT
Mpl] Mu
Qp. Qi = Softmmx
Qp
Qp.Qr LN(QP),
(8)
where N,N,, denotes the zero matrix, Mpt E NANNi My E NiN and LN demotes the layer normalization.
Point-Lane Deformable Cross Attention. After self-attention, Qp and Qt used compute deformable cross- ansseatttntionn with the BEV feature. Specifically, we independently initialize two sets of learnable reference points, R, and R₁, corresponding Qp and Qı, which attends to the BEV feature via deformable cross-attentio using its own reference points. The results are then passed through two separate ferd-rd networks (FFNs). The process is described as follows:
Qp. Qi = LN(DeformAttn Qp: Rp, Fbee)) LN(DeformAttt(( (Qi, Rí, Fbev))
(9)
Qp. Qi = LN(FFF( LN(FFN((.))
(10)
where Rp E N3 RG E IR X3x Fbee E Hg xd denotes BEV feature map, (HB, WB) denotes the BEV size of Fbeu-
Unified Scene Graph Network. We construct a Unified Scene Graph Network by assembling the Qp- Qi and Q., as illustrated in Figure a(). To enhance the interaction between point and lane representations, we further introduce the Point-Lane Graph Convolutional Network (PLGCN), as shown in FigureBjb, The PLGCN is designed to facilitate bidirectional feature aggregation between Qp and Q₁ based on their geometric relationships. The structure of the PLGCN as follows:
Ap Ap A2Mpt
(11)
5

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2p = GCN (pt: + Qp. Qi = 1 + QQ
(12)
In the Unified Scene Graph Network, Op and Qi first interact with each other through the first Point-Lane Graph Convolutional Network (PLGCN1) to generate updated features Q2 and Q/ Then Q} is processed through two separate GCNs: GCNz aggregates information from Qi itself to enhance intra-lane relationships, while GCNμ, aggregates information from Q to incorporate semantic context. The outputs from these two branches are concatenated and downsampled to form Q7 Finally, second round of Point-Lane Graph Convolutional Network (PI.GCN2) is applied to Qf and Qp yielding the final enhanced features Qi and Q3 which are used as the output of the Point-Lane detector decoder layer. The overall process can be formulated as:
Qp Q
PLGCNI (Qp, Q1, Mpt. Gp))
(13)
Qi Downsample (Concat (GCNu(Q) Ma) + Qt GCNu(Qi, Ga) Q())
(14)
27 Q 2p. Q O₂ Qi
p Q7, Mp,, Gpi)
(15)
(16)
where A1, A2 denotes the learnable parameters. GCN A) o (AXW), X denotes the input, W denotes the learnable weight matrix, A denotes the adjacency matrix, Á denotes the normalized A and denotes sigmoid[42) function. Mu I + Mu + MM. I E ER XNN, denotes the identity matrix, Mpt. Mu is derived within the Point-Lane Merge Self-Attention, Gpt.Grt is derived within the Topology Head from the previous decoder layer. Downsample denotes the Linear-layer.
Point-Lane Head. After passing through the Unified Scene Graph Network, we obtain the enhanced point query Op and lane query Q1, which are fed into the PointHead and I LaneHead, respectively, to produce the predicted point set P = {Preg: Pota) } and lane set L = (ireg Lds). as follows:
I = (ointHead((p)) L = LaneHead (Qi)
(17)
where Preg E RRNxX3 and Lreg E RRNxXXx3 denote the regressed points and lanes, respectively, Pota E 11 and Lote E IR Nx1 denotes classification scores for points and lanes, LeneHead and PointHead each consist of two separate MLP branches for regression and classification.
3.5 Topology Head
To predict the point-lane topology, lane-lane topology and lane- traeftaffic topology. We perform topology reasoning based on the enhanced features 2,. Qi and Q obtained from the detectors. We encode these features using separate MLPs and compute their pairwise similarities as the topology reasoning outputs. The process is formulated as follows:
Gpt SiLPoid(MLP((,)) T))
(18)
GR SigPO)d(MLP(()) MLP(()) ))
(19)
G₁ = (iLPO)d(MLP(()) TL)(
(20)
where Gpt E RRNN denotes the point-lane topology, Gn N..N denotes the lane-lane topology, Gie, E NNNN denotes the lane-traffic topology.
3.6 Training
During the training phase, the overall loss of TopoPoint is composed of detection loss and topology reasoning loss. The detection loss includes the traffic element detection loss, point detection loss and lane detection loss. The topology reasoning loss consists of the point-lane topology loss, lane-lane topology loss and lane-traffic topology loss. The total loss is defined as:
Liotal XrC; + XpCp + XeLi + Apt + AuLu + Areh
(21)
where L., Cp and L, denote the traffic element detection loss, point detection loss and lane detection loss, respectively. Lut, Lu and Lue represent the losses for point-lane topology, lane-lane topology and lane-traffic topology reasoning. Xe, Ap. \ı. Apt. du and λ are the corresponding loss weights. Specially, the C, and L, consist of classification loss and regression loss, where the classification loss employs the Focal loss 43 and the regression loss utilizes the I.1 loss 44 For Lt, in addition
to classification loss and regression loss, we incorporate the GloU loss 45 to further improve
localization accuracy. For topology reasoning, we adopt the focal loss for both Cpt. Lu and Cu.

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3.7 Inference
To mitigate the endpoint deviation issue lane prediction during inference, we propose the Point- Lane Geometry Matching (PLGM) algorithm. This method first filters out high-confidence predictions from Preg and freeg using their associated classification scores Pats and Lass. For each selected point P; Prelect, identify distances in the BEV space. If the matching is found, the selected point and its neighboring lane endpoints are jointly averaged to compute refined endpoint É;, which is then used to update the corresponding lane predictions. This refinement leads to better-aligned lane endpoints and improved overall topology consistency. The complete procedure is illustrated in Algorithm
of nearby lane endpoints NG from Lastect based their geometric
Algorithm 1:
Point-Lane Geometry Matching Algorithm
Input: Predicted points Preg. Pots: predicted lanes Breg Lass classification thresholds Tp. 715 geometry distance threshold
Output: Refined lanes Lre/
Step 1: Sigh-Confidence Filtering
Filter points with high classification scores: Pretect {P Pi >
Tp)
Filter lanes with high classification scores: Listect (izea
L'sts > mi
Step 2: Geometry-Basee Matching and Refinement
foreach point P. E Prelect do Initialize empty match set: N₁ 0; foreach lane Ls LLuttct do if distancel P., f.
j.endpinnty < then
Add L5 No
AG * / then Compute refined endpoint:
E, WitT (P. EL,eN, Leuapoonn):
Update endpoints of all Li E Ni with Es;
return Liee with refined endpoints
where Preg E RRNx Freg E Nixkx3 Pata E RRNx11 and i.d. E NNx1, Np denotes the number of point query, N₁ denotes the number of lane query, and k denotes the number of points in each lane.
4
Experiment
4.1 Dataset and Metric
Dataset. We evaluate TopoPoint on the large-scale topology reasoning benchmark OpenLane- [7] which is constructed based on (rg)verse2[46] and nuScenes 47). The dataset provides comprehensive annotations for lane centerline detection, traffic element detection, and topology reasoning tasks. OpenLane- V2 is divided into two subsets: subser and subset B, each containing 1,000 scenes captured at 2 Hz with multi-view images and corresponding annotations. Both subsets include annotations for lane centerlines, traffic elements, lane-lane topology, and lane-traffic topology. Notably, subset_A A provides seven camera views as input, while subset B includes six views.
Metric. We adopt the evaluation metrics defined by OpenLane-Vi including DET, DET,, TOP and TOP all of which are computed based on mean Average Precision (mAP). Specifically, DET; quantifies similarity by averaging the Fréchet distance under matching thresholds of 1.0, 2.0, and 3.0. DET, evaluates detection quality for traffic elements using the Intersection over Union (IoU) metric, averaged across different traffic categories. TOP and TOP;: measure the similarity of the predicted lane-lane topology matrix and lane-traffic topology matrix, respectively. The overall OpenLane- V2 Score (OLS) calculated as follows:
OLS DET, DET, + VTOPR + [TPP,
(22)
7

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All evaluation metrics are computed based on the latest version (v2.1.0) of OpenLane- V2, which is available on the official OpenLane- V2 GitHub repository] In addition, to evaluate the performance of endpoint detection, we define a custom metric DETp, p. which is computed as the average over match thresholds = (1.0, 3.0) based on the point-wise Fréchet distance, as follows:
1 DET,
ΣAA AP SET
(23)
IT
4.2 Implementation Details
Model details. The multi-view images have a resolution of 2048 x 1550 pixels, with the front view specifically cropped and padded to match 2048 1550. Notably, all multi-view inputs are downsampled by factor of 0.5 before being fed into the backbone, except for the front view, which directly processed at the original resolution. pretrained ResNet-50 is adopted as the backbone, and a Feature Pyramid Network is used as the neck to extract multi-scale features. The hidden feature dimension d is set to 256. BEV grid size is configured to 200 x 100. The number of traffic element query No, point query Nº and lane query N1 are set to 100, 200 and 300, respectively. The sampled points number of each lane is set to 11. The decoder consists of 6 layers. Following TopoLogic, the learnable parameters À and in the mapping function Smap are initialized to 0.2 and 2.0, respectively, A and A2 in Apt are both initialized to 1.0. The detection loss weights A, Ap λ and are all set to 1.0, while the topology reasoning loss weights λn and dy both set to 5.0. In inference, the classification thresholds for filtering high-confice cogfcconfieence predictions are both set to Tp = 7 0.3. For geometric matching, the distance threshold d is set to 1.5 meters to determine valid point-lane associations.
Training details. We train the traffic detector, point-lane detector and topology head in end-to-end manner. TopoPoint is trained using the AdamW optimizer with a cosine annealing learning rate schedule, starting at 2.0 x 10-4 with weight decay of 0.01. All experiments are conducted for 24 epochs on 8 Tesla V100 GPUs with a batch size
4.3
Comparison on OpenLane- V2 Dataset
We compare TopoPoint with existing methods on the OpenLane-VV benchmark, and the results are summarized in Table [ On subset_A, TopoPoint achieves 48.8 on OLS, surpassing all previous approaches and achieving state-of-the-art performance. Notably, despite TopoFormer leveraging a pretrained lane detector, our method achieves superior performance (48.8 v.s. 46.3 on OLS). Built upon TopoLogic, TopoPoint demonstrates superior performance in lane detection (31.4 v.s. 29.9 on DET,) and shows substantial improvement in traffic element detection (55.3 v.s. 47.2 on DET,). Furthermore, it outperforms in lane-lane topology reasoning (28.7 v.s. 23.9 on TOP,) and achieves better results in lane-traffic topology reasoning (30.0 v.s. 25.4 on TOP;). Additionally, there is a notable improvement in the endpoint detection (52.6 v.s. 45.2 on DET,). Meanwhile, TopoPoint also achieves state-of-the-aat performance on subset (49.2 on OL.S, 45.1 on DFT,), further demonstrating its effectiveness.
4.4 Ablation Study
We conduct ablation studies several key components of TopoPoint using OpenLane- V2 subsett A.
Impact of each module. We conduct an ablation study to assess the impact of each module on topology reasoning performance. As shown in the Table 2 keeping the original front-view scale (scale =1.0) improves traffic element detection (53.8 v.s. 46.8 on DET,), enhancing lane-traffic topology reasoning (27.0 v.s. 24.3 TOP;). Adding Point-Lane Merge Self- Self-Attention (PLMSA) boosts lane and endpoint detection (30.2 v.s. 29.4 on DET, 49.8 v.s. 44.8 on DET,)) leading to better lane-lane and lane-traffic topology reasoning (27.2 v.s. 23.8 on TOPn, 28.5 v.s. 27.0 on TOP,).
Incorporating Point-Lane Graph Convolutional Network (PLGCN) further improves detection (30.8 30.2 on DET, 51.8 v.s. 49.8 on DET,), p). Finally, the Point-Lane Geometry Matching (PLGM) algorithm refines lane endpoints during inference, mitigating endpoint deviation and enhancing lane and point detection (31.4 v.s. 30.8 on DET, 52.6 51.8 DET,).
Effect of different GCNs. We investigate the impact of various GCN designs on topology reasoning performance. As shown in Table 3. adding the lanc-lane GCN and lane-traffic GCN improves lanc
8

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Table 1: Performance comparison on OpenLane-V2 Results are from TopoLogic and TopoFormer papers. TopoFormer' utilizes a pretrained detector. The DET, scores for TopoNet, TopoMLP, and TopoLogic are computed using their official codebases. denotes the absence of relevant data.
Data
Method
Conference
DET1
DET,
ToP
T
DET, OLS1
STSU[I
ICCV2021
12.7
43.0
2.9
19.8
29.3
VectorMapNett
ICML2023
11.1
41.7
2.7
9.2
24.9
MapTRR
ICLR2023
17.7
43.5
5.9
15.1
31.0
TopoNet
Arxiv2023
28.6
48.6
10.9
23.8
39.8
43.8
subser_ A
TopoMLPP22
ICLR2024
28.3
49.5
21.6
26.9
44.1
43,4
Topol.ogic 15
NeurIPS2024
29.9
47.2
23.9
25.4
44.1
45.2
TopoFormer" [31
CVPR2025
34.7
48.2
24.1
29.5
46.3
TopoPoint (Ours)
31.4
55.3
28.7
30.0
48.8
52.6
STSU[13
ICCV2021
8.2
43.9
VectorMapNer(11)
ICML2023
3.5
49.1
MapTR 48
ICLR2023
15.2
54.0
TopoNet 26
Arxiv2023
24.3
55.0
6.7
16.7
36.8
38.5
subser_ B
TopoMLPP(29
ICIR2024
26.6
58.3
21.0
19.8
43.8
39.6
TopoLogic [5]
NeurIPS2024
25.9
54.7
21.6
17.9
42.3
39.2
TopoFormer" [3]
CVPR2025
34.8
58.9
23.2
23.3
47.5
TopoPoint (Ours)
31.2
60.2
28.3
27.1
49.2
45.1
Table 2: Ablation study on different modules.
Table 3: Ablation study on different GCNs. "w/o
Baseline is reproduced using TopoLogic code.
GCN" denotes removal of Unified Graph Network.
Module
DET DET, TOP TOP;
OLS
DET,
Module
DET, DET, TOP,
TOP
OLSt DET,"
Baseline
29.2
46.8
23.4
24.3
43.4
44.5
wlo GCN
28.9
53.9
25.6
26.4
46.2
48.6
FVScale
29.4
53.8
23.8
27.0
46.0
44.8
GCNN
29.8
54.2
26.9
27.
47.0
49.8
PLMSA
30.2
54.8
27.2
28.5
47.6
49.8
GCN,
30.6
54.5
27.4
28.8
47.8
50.5
PLGCN
28.0
29.2
48.3
51.8
PLGCN,
30.9
55.0
28.2
29.5
48.3
51.9
PLGM
28.7
30.0
52.6
PLGCN2
31.4
55.3
28.7
30.0
52.6
detection (30.6 v.s. 29.8 v.s. 28.9 on DET;), thereby enhancing both lane-lane and lane-traffic
topology reasoning (27.4 v.s. 26.9 25.6 TOP,, 28.8 v.s. 27.1 v.s. 26.4 on TOP;). Moreover, introducing two variants of the point-lane GCN effectively boosts both lane and endpoint detection performance (31.4 v.s. 30.9 v.s. 30.6 on DET, 52.6 v.s. 51.9 v.s. 50.5 on DET,).
Image scales set up. We investigate the impact of different image scaling strategies on topology reasoning performance. As shown in the Table keeping the front-view image its original resolution improves the performance of traffic element detection (55.3 v.s. 48.6. 54.7 v.s. 48.3 on DET,). On the other hand, downscaling the multi-view images by factor of 0.5 slightly boosts lane detection performance (31.2 v.s. 30.5, 31.4 v.s. 30.8 on DET;).
Effect of point and lane query numbers. We investigate the impact of varying the number of point and lane query on topology reasoning performance. As shown in the Table increasing the number of point query from 100 to 200 improves endpoint detection (51.8 turn enhances lane detection performance (30.7 v.s. 29.5 on DET,). However, further increasing negative point samples, leading degraded endpoint detection (51.4 v.s. 52.6 on DET,) and consequently worse lane detection performance (30.8 v.s. 31.4 DET,). On the other hand, increasing the number of lane query from 200 to 300 consistently improves lane detection accuracy(31 (31.4 v.s. 30.7 on DET,).
49.7 DET,), which in
the number from 200 to 300 introduces
4.5 Qualitative Results
Figure provides qualitative result comparison between TopoLogic and our TopoPoint. whole, both Topologiic and TopoPoint yield good results. Nevertheless, TopoLogic lacks direct enhancement lane detection itself, is more likely to produce incorrect or missing lanes, thereby resulting inaccurate absent topologies. Benefit from the independent endpoint modeling and the

---

Table 4: Ablation study on front-view scale and milti-view scale. Syv denotes the scale of front- view, Smv denotes the scale of multi-view.
Table 5: Ablation study on number of point query and lane query. Np denotes the number of point query, Ni denotes the number of lane query.
Stu Siee
DET
DET, TOP T0P
OL.S
D
N, N;
DET DET,
TOP
TOP
OLS DET, 1
0.5
0.5
31.2
48.6
28.5
28.4
46.6
52.3
100 200
29.5
54.3
25.6
27.0
46.5
49.7
0.5
1.0
30,5
48.3
28.0
27.9
46.1
51.5
200 200
30.7
53.7
27.4
28.2
47.5
51.8
1.0
0.5
31,4
55.3
28.7
30,0
48.8
52.6
200 300
31.4
55.3
28.7
30,0
48.8
52.6
10
1.0
30.8
54.7
28.3
28.9
48.1
51.8
300 300
30.8
54.6
28.2
29.8
48.3
51.4
Nulti-view Input
Topol.ogic
TupoPuint
TopiLoogic
Lane Topology
Comore Topalagy
Nove
Topelogy
Node
Qualitative comparison of Logi and our TopoPoint. The first row denotes multi- view inputs, and the second row denotes lane detection result with lane topology result. In the graph form of lane topology, node indicates lane while edge indicates lane topology, where green/red/blue color respectively indicates the correct/wrongh/e wrong//wwooggmmsseedd prediction.
Figure 4:
interaction between points and lanes, TopoPoint has managed avoid such situations as much as possible. Moreover, it is evident that TopoPoint eradicates the endpoint deviation at lane connections, which still exist in TogoLogic. Both Figure and Figure more qualitative results comparison between TopoLogic and our TopoPoint.
5 Conclusion
In this paper, we identify the endpoint deviation issue in existing topology reasoning methods. To tackle this, we propose TopoPoint, which introduces explicit endpoint detection and strengthens point-lane interaction through Point-Lane Merge Self-Attention and Point-Lane GCN, We further design geometry matching strategy refine lane endpoints. Experiments on OpenLane-V V2 show that TopoPoint achieves state-of-the-art performance in OLS. Additionally, we introduce DET, metric for evaluating endpoint detection, where TopoPoint also achieves significant improvement.
Impact. TopoPoint improves 3D lane detection by addressing endpoint deviation and enhancing topology reasoning, benefiting autonomous driving tasks like planning and mapping.
6 Acknowledgements
This work is supported by National Key R&D Program of China (2033YFD2000303) and National Natural Science Foundation of China (62372433).
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Multi-view Input
Endpoint & Lane Defection
Lane- Lane Topology
Front- view Lane- Mane-Traffie Traffic Topology
GT
TopoLogic
TopoPoint
GT
TopoLogie
TopoPoint
Figure 5: Additional qualitative comparison of TopoLogic and TopoPoint. The first denotes multi-view inputs, the second row denotes the endpoint detection and lane detection results, where the lane endpoints indicated by red dots. The third row denotes the lane-lane topology result, and the last row denotes traffic element detection and lane-traffic topology results in the front-view.
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Multi-viee Input
Endpoint & Lane Detection
Lane-Lane Topology
Lane- Lane-Traffie Traffic Topology
Front ve
GT
TopoLogic
Toio
GT
TopoLogic
TopoPoint
Figure 6: More qualitative comparison of TopoLogic and TopoPoint. The first row denotes multi-view inputs, the second row denotes the endpoint detection and lane detection results, where the lane endpoints are indicated by red dots. The third row denotes the lane-lane topology result, and the last row denotes traffic element detection and ane-traffic topology results in the front-view.
22


Reviews:
[{"review_id": "talo0efXZI", "reviewer": "Reviewer_VkTs", "summary": "This paper proposes a topology reasoning method in Bird’s-Eye View (BEV). The core idea is to address the problem of *diverged endpoints*—where lanes that should share the same endpoints are predicted as having different endpoints. The authors introduce corresponding network designs, including point query, point-lane decoder, and geometry matching, which are technically sound.", "questions": "Although this method achieves competitive performance compared to existing approaches, the accuracy of HD topology reasoning remains far from practical application. I would like to hear the authors’ thoughts on the following considerations:  \n\n1. **Data Sufficiency Hypothesis**: Are current methods theoretically sufficient for HD topology reasoning, with performance limitations primarily due to insufficient training data? Would more data alone resolve these issues?  \n2. **Inherent Limitations Hypothesis**: Even with abundant data, might current methods still fail to fully address the problem, suggesting fundamental limitations in the approach?", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The quality of this work is high. The paper is well-written and easy to follow. The proposed design effectively tackles topology reasoning challenges and holds significant value. The originality of the approach is relatively strong.", "was_revised": true, "final_justification": "The overall method is technically sound and good. I have no further questions and I keep my positive rating.", "rebuttal": "**Thank you very much for high recognition  on our work. We are glad to discuss these open questions you raised with you.**\n\n**Q1:** Although this method achieves competitive performance compared to existing approaches, the accuracy of HD topology reasoning remains far from practical application. I would like to hear the authors’ thoughts on the following considerations:\n\n1. **Data Sufficiency Hypothesis**: Are current methods theoretically sufficient for HD topology reasoning, with performance limitations primarily due to insufficient training data? Would more data alone resolve these issues?\n2. **Inherent Limitations Hypothesis**: Even with abundant data, might current methods still fail to fully address the problem, suggesting fundamental limitations in the approach?\n\n**A1:** We sincerely thank you for the insightful question. Regarding the two hypotheses you raised, we believe that the performance of HD topology reasoning is currently constrained by both the scale of available data and limitations of existing model architectures. We address each point below:\n\n- **On the role of data**: Increasing the amount of training data does lead to improved performance. For a complex task like HD topology reasoning, large-scale and diverse datasets are essential for achieving robust generalization. However, it is equally important to ensure consistent and high-quality annotations, such as precise definitions of lane endpoints and other topology-critical elements, during data collection and labeling.\n- **On model limitations**: Current methods still have considerable room for improvement. Many existing approaches rely on DETR-like architectures to detect lane centerlines, which may not be fully reliable. Similarly, using simple MLPs to model topological structures can fall short in capturing the complex spatial and relational dependencies inherent in driving scenes. As research progresses, we anticipate that more specialized and expressive architectures will emerge to better capture the nuances of topology reasoning.\n- **On system-level iteration**: Building a reliable AI system requires co-evolution of both data and model design. Our proposed method has demonstrated its effectiveness on a reasonably large-scale benchmark (OpenLane-V2), achieving state-of-the-art performance. However, we acknowledge that broader deployment in industrial-scale settings would require further adaptation and iteration. We are excited about potential applications of  TopoPoint on large-scale, real-world datasets to further validate and refine its capabilities\n\n**If you have any further questions or suggestions about our article, we'd love to discuss our content with you.**"}, {"review_id": "Bo3UUyo5xL", "reviewer": "Reviewer_gBnP", "summary": "This paper focuses on the topology reasoning task in autonomous driving. Previous methods first predict independent lanes and then use a topology head to generate a lane-lane adjacency matrix, but they suffer from endpoint deviation (or inconsistent endpoints). To address this, the authors propose jointly predicting lanes and endpoints, then matching lanes to shared endpoints to ensure consistency. Experiments on the OpenLane-V2 benchmark demonstrate that the proposed method outperforms SOTA methods.", "questions": "1. Conduct more fair comparison by removing the image scale trick or adding this trick to SOTA methods.\n2. The authors should analyze why TopoPoint underperforms TopoFormer in DET_l (lane detection accuracy). Can TopoFormer's architecture benefit from the proposed endpoint strategy to achieve comparable gains?\n3. What about adopt PLGM to baseline methods? If lane detection is sufficiently accurate, the Point-Lane Geometry Matching (PLGM) module could be independently validated on baseline methods (without endpoint detection) to assess its standalone contribution to topology reasoning.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. The idea of introducing endpoints to improve lane topology reasoning is both intuitive and effective.\n2. The proposed method achieves state-of-the-art performance on the OpenLane-V2 benchmark.\n3. Ablation studies demonstrate that the proposed components (point-lane attention, point-lane GCN, and geometry matching) each contribute to improving OLS performance.\n\nWeaknesses\n1. The authors use high-resolution front-view images while downsampling other views by 0.5×, which alone improves OLS from 43.4 to 46.0. To isolate the impact of endpoint detection, this strategy should either be removed or applied uniformly to SOTA methods. Otherwise, TopoPoint's gains may be attributed to this resolution trick rather than topology reasoning.\n2. Despite superior topology metrics, TopoPoint lags behind TopoFormer in DET_l. The author should explain why the proposed method can no achieve better lane detection performances.", "was_revised": true, "final_justification": "This paper proposes a effective method to improve the topology reasoning performances, and the author's rebuttal addressed my concerns. I keep my positive rating.", "rebuttal": "**Thank you very much for carefully reviewing our paper and providing in-depth feedback. We hope our response and clarifiction can ease some of your concerns.**\n\n**Q1:** TopoPoint's gains may be attributed to this resolution rather than topology reasoning. More fair comparison by removing the image scale or adding this trick to SOTA methods need to be conducted.\n\n**A1:** We fully understand your concerns about the FV resolution. Fortunately, we have conducted comprehensive ablation studies on the paper (as shown in Tables 2 & 4 of the paper). \n\n- First, it is important to clarify that OLS is an aggregated metric (calculated as $OLS = DET_l + DET_t + \\sqrt{TOP_{ll}} + \\sqrt{TOP_{lt}}$). Its improvement can be traced back to other sub-metrics, allowing us to understand the source of the improvement.\n\n- According to the results in Table 4, using a front view with 1.0x resolution mainly affects traffic-related metrics (48.6 → 55.3 on $DET_t$, 28.4 → 30.0 on $TOP_{lt}$), while having little impact on lane-related metrics (31.2 → 31.4 on $DET_l$, 28.5 → 28.7 on $TOP_{ll}$). Additionally, similar results are observed in Table 2 between the *Baseline* and *Baseline+FVScale*. **The core of TopoPoint lies in the explicit modeling and co-optimization of lane endpoints, which is primarily reflected in the metrics of $DET_l$, $TOP_{ll}$, and $TOP_{lt}$ . This improvement can be considered independent and orthogonal to that brought by resolution adjustments.**\n\n- The results in the first row of Table 4 ($S_{fv}=0.5$, $S_{mv}=0.5$) can actually be fairly compared with other methods in Table 1,  and some results are  integrated from Tables 1 and 4 as follows:\n\n  | Model | $DET_l$ | $DET_t$ | $TOP_{ll}$ | $TOP_{lt}$ | $OLS$ | $DET_p$ |\n  | ---------- | ----------- | ------------ | ---- | ---------- | ----------- | ---------- |\n  | TopoLogic (Tabel1)                    | 29.9  | 47.2  | 23.9   | 25.4   | 44.1 | 45.2 |\n  | TopoPoint (Table4, FV-0.5x)              | 31.2  | 48.6  | 28.5   | 28.4   | 46.6 | 52.3 |\n  | TopoPoint (Tabel4, FV-1.0x) | **31.4** | **55.3** | **28.7** | **30.0** | **48.8** | **52.6** |\n\n- **Increasing the front view resolution is simple to implement, yields significant effects, and incurs little computational overhead since it is only used in the traffic element branch, as verified by follow table.** Regrettably, this noteworthy strategy has been overlooked by previous studies. Therefore, we hope to share this strategy to promote research and applications in this field.\n| Model     | FLOPs  | #PARAMS | FPS  |\n| --------- | ------ | ------- | ---- |\n| TopoPoint (FV-0.5x) | 989.3G | 67.9M   | 14.8 |\n| TopoPoint (FV-1.0x) | 1077.7G | 67.9M   | 14.4 |\n\n\n----\n\n**Q2:** Why TopoPoint underperforms TopoFormer in $DET_l $ (lane detection accuracy)? Can an TopoFormer's architecture benefit from the proposed endpoint strategy to achieve comparable gains?\n\n**A2**: To clarify why TopoFormer achieves a higher $DET_l$, it is necessary to elaborate on its training strategy, which is distinctly different from that of other methods.\n\n- **As noted in Section 4.3 (lines 243–244) and the caption of Table 1, TopoFormer employs a two-stage training strategy involving a pre-trained lane detector. In contrast, other baselines as well as our proposed  TopoPoint are trained using a simpler end-to-end approach, as described in Section 4.2 (line 236)**. This fundamental difference in training strategy gives TopoFormer an inherent advantage in lane-related metrics, and should be taken into account when comparing performance.\n- Theoretically, TopoFormer can benefit from the proposed endpoint strategy. Unfortunately, perhaps because CVPR 2025  was held in June, the official repository provided by TopoFormer has not included any code as of today (only a README), and the training details of the pre-trained detector have not been disclosed. Therefore, we cannot conduct effective verification on this for the time being.\n\n----\n\n**Q3:** What about adopt PLGM to baseline methods?\n\n**A3**: This idea is very constructive, as it means that PLGM can be directly integrated into other models as a training-free component to improve performance. We conducted experiments as shown in the table below.\n\n | Model | $DET_l$ | $DET_t$ | $TOP_{ll}$ | $TOP_{lt}$ | $OLS$ | $DET_p$ |\n | ----------------- | -------- | -------- | -------- | -------- | -------- | -------- |\n | TopoNet           | 28.6     | 48.6     | 10.9     | 23.8     | 39.8     | 43.8     |\n | TopoNet (+PLGM)   | 29.3     | 48.6     | 11.7     | 24.6     | 40.4     | 44.9     |\n | TopoLogic         | 29.9     | 47.2     | 23.9     | 25.4     | 44.1     | 45.2     |\n | TopoLogic (+PLGM) | 30.5     | 47.2     | 24.6     | 26.0     | 44.6     | 46.1     |\n | TopoPoint         | **31.4** | **55.3** | **28.7** | **30.0** | **48.8** | **52.6** |\n\nThe results indicate that the introduction of PLGM can yield improvements in TopoNet and TopoLogic. Further improvements still require explicitly modeling and learning endpoints, thereby enhancing performance within the model itself.\n\n**We hope this response could help address your concerns, and wish to receive your further feedback soon.**"}, {"review_id": "ZfGWcfIKOg", "reviewer": "Reviewer_P95Y", "summary": "This paper introduces TopoPoint, a framework aimed at improving topology reasoning in autonomous driving by explicitly detecting lane endpoints and jointly modeling lanes and endpoints. The authors identify the persistent issue of endpoint deviation in existing lane topology models and address it by proposing: (1) Point-Lane Merge Self-Attention for to exchange global information between endpoints and lanes, (2) Point-Lane Graph Convolutional Network for message passing between lanes and endpoints, and (3) Point-Lane Geometry Matching for refinement during inference. Evaluations on the OpenLane-V2 dataset demonstrate state-of-the-art performance across multiple metrics, including the newly proposed $DET_p$ metric for endpoint detection. Ablation studies show consistent improvements from each module.", "questions": "* Given the complexity of TopoPoint, can the authors provide runtime benchmarks? Is the system deployable at real-time rates?\n* Have the authors considered evaluating TopoPoint on datasets with different geographic or environmental conditions (e.g., KITTI, DENSE)? Qualitative or quantitative comparisons on such datasets would provide insights on the robustness of TopoPoint.", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n* Results on OpenLane-V2 are consistently strong, outperforming prior works such as TopoLogic and TopoFormer across the sub-tasks.\n* The improvements brought by each component of TopoPoint are well-documented in the ablation study.\n\n**Weaknesses**\n* The use of a fixed number of queries could limit adaptability in dense or highly dynamic traffic scenes, as noted in the paper.\n* As acknowledged in the checklist, all results are based on single experiment runs without variance estimates.\n* The method is evaluated exclusively on data from Argoverse and nuScenes (U.S. and Singapore) under primarily favorable weather. This leaves open questions about performance under adverse conditions or in new geographical domains.", "was_revised": true, "final_justification": "The paper introduces novel contributions with strong results and ablations. While I understand that single-run experiments are common in prior works, I believe this is not the best scientific practice. Therefore, I'm keeping my current (overall positive) assessment.", "rebuttal": "**Thanks for your careful reading of our paper. We hope our response and clarifiction can ease some of your concerns.**\n\n**Q1:** Given the complexity of TopoPoint, can the authors provide runtime benchmarks? Is the system deployable at real-time rates?\n\n**A1:** As you noted, real-time performance is an important consideration for deployment. Following your suggestion, we evaluated the computational cost of our model on Tesla A100 GPU. **The results show that although TopoPoint introduces slightly higher resource consumption compared to the baseline, the increase is marginal.** Thanks to its compact parameter size, TopoPoint remains easy to deploy and demonstrates favorable real-time performance.\n\n| Model     | FLOPs  | #PARAMS | FPS  |\n| --------- | ------ | ------- | ---- |\n| TopoNet   | 712.1G | 38.6M   | 16.6 |\n| TopoLogic | 665.0G | 37.8M   | 17.1 |\n| TopoPoint | 989.3G | 67.9M   | 14.8 |\n\n----\n\n**Q2:** Have the authors considered evaluating TopoPoint on datasets with different geographic or environmental conditions (e.g., KITTI, DENSE)? Qualitative or quantitative comparisons on such datasets would provide insights on the robustness of TopoPoint. This leaves open questions about performance under adverse conditions or in new geographical domains.\n\n**A2**:In terms of practical applications, adapting to different weather conditions and regions is crucial. Unfortunately, due to the constraints of annotation costs, there are no additional datasets available in the emerging field of topology reasoning at present. OpenLane-v2 is already the largest and widely recognized dataset to date. **While datasets like KITTY and DENSE are also related to autonomous driving, they do not provide topological annotations, which makes it impossible to conduct evaluations for the time being.**  Moreover, the camera configurations in the KITTI and DENSE datasets differ significantly from those in OpenLane-V2, making it infeasible to directly apply TopoPoint for inference on these datasets.\n\nIn the future, we will also take these factors into account to promote the practical implementation of topology reasoning.\n\n----\n\n**Q3:** The limitation in Limimataion Section or checklist:\n\n- The use of a fixed number of queries could limit adaptability in dense or highly dynamic traffic scenes.\n- All results are based on single experiment runs without variance estimates.\n\n**A3**: These limitations are indeed mentioned in the paper, but they are prevalent across various research efforts in the field of topology reasoning.\n\n- Currently, mainstream topology reasoning models are all based on the DETR-like architecture, where the number of queries can only be fixed at present.\n\n- **Existing topology reasoning methods (e.g. TopoNet, TopoMLP, TopoLogic) are evaluated based on single experiments. For one thing, single experiments on large-scale datasets can basicly verify the effectiveness;** for another, multiple experiments with deep learning methods consume significant computational resources.\n\n**We hope this response could help address your concerns, and wish to receive your further feedback soon.**"}, {"review_id": "Q2AUqJLiGO", "reviewer": "Reviewer_HgsV", "summary": "This paper aims to solve the endpoint deviation problem in lane-phase inference, which is central to understanding autonomous intersections.  The authors added a separate branch for detecting endpoints and designed Point-Lane Merge Self-Attention and Point-Lane  Graph Convolutional Network to exchange global information between endpoints and lane queries.  During the inference stage, the predicted lane endpoints are aligned with the detected endpoints through geometric matching to correct remaining errors. The method demonstrated its effectiveness by achieving state-of-the-art performance on the OpenLane-V2 benchmark.  The introduction of the $DET_p$ metric further enhances the work's originality by providing a new tool for evaluating a previously less quantified aspect of lane detection performance.", "questions": "1. Analysis of Inter-Module Error Propagation:\nWhether the performance degradation of ($DET_t$) only affects the lane-traffic topology ($TOP_lt$), or whether it can also negatively affect the geometric accuracy of lane($DET_l$) and endpoint ($DET_p$) predictions through feature propagation.\n\n2. Verification of Generalization Performance on Other Datasets:\nAll experiments in this paper were performed only on the OpenLane-V2 benchmark. To prove the generalizability of the TopoPoint methodology, we would like to know if there are additional experimental results on other tasks in nuScenes or Argoverse2, or on different datasets (e.g., Waymo Open Dataset).\n\n3. Sensitivity on calibration accuracy:\nTopoPoint explicitly acknowledges that it relies on accurate calibration, could you provide sensitivity analysis experimental results showing the changes in each evaluation indicator for each correction error range.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "[Strengths]\n1. Novel Solution for endpoint detection in autonomous driving:\nThis paper identifies and emphasizes the importance of the “endpoint deviation” problem, which is practical but often overlooked in the field of autonomous driving topology inference. It specifically explains how minor discrepancies at the endpoints that occur when predicting multiple lanes independently can cause problems for subsequent modules. As a solution, the core idea of explicitly detecting lane endpoints as separate, independent entities and using them to refine lane predictions and enforce topological consistency is highly innovative and directly addresses the identified problem.\nThe core idea of explicitly detecting lane endpoints as separate entities and then using them to refine lane predictions and reason about topology is novel and directly tackles this deviation problem.\n\n2. Novel metric for endpoint detection:\nTo directly demonstrate the improvement in endpoint detection accuracy, we propose a new evaluation metric, $DET_p$. This metric shows a significant performance improvement over existing methods, providing strong quantitative evidence to support the claims made in this paper, from problem definition to verification.\n\n3. Detailed and Reproducible Methodology:\nThe paper provides detailed explanations and mathematical formulas for each component of the proposed TopoPoint framework. From the geometric bias calculation of PLMSA to the complex GCN interactions within USGN, the learning loss function configuration, and hyperparameter settings, it provides sufficient information necessary for research reproduction.\n\n[Weaknesses]\n1. Lack of Real-time and Computational Cost Analysis:\nThere is no analysis of the inference speed (FPS) or computational cost (GFLOPs) of the proposed model. The TopoPoint architecture, especially USGN, which includes multiple GCN modules, is conceptually complex and is expected to require significant computational power. Real-time performance is critical for autonomous driving systems, so it is necessary to analyze whether the performance improvement compared to simpler models justifies the additional computational cost.\n\n2. Proof of Impact on Downstream Tasks:\nThe paper argues that solving the endpoint deviation problem is important for subsequent path planning and control tasks, but it does not provide direct experimental evidence to support this claim. If a quantitative evaluation of how much improved topological accuracy actually improves the performance of the planning module had been included, the practical usefulness of the proposed method and the logical persuasiveness of the paper would have been greatly enhanced.\n\n3.  Lack of calibration sensitivity analysis:\nIn their conclusion, the authors acknowledged that TopoPoint relies on accurate camera calibration, but they did not provide a sensitivity analysis of how performance degrades depending on the degree of calibration error. This is important information for evaluating the robustness of the model to sensor errors that may occur in actual vehicle applications.", "was_revised": true, "final_justification": "It would be good to add the above-mentioned experiments to the paper. The rebuttal addressed my concerns. I am keeping my rating as positive.", "rebuttal": "**Thank you very much for your high recognition of our work. We hope our response and clarifiction can ease some of your concerns.**\n\n----\n\n**Q1:**  Lack of Real-time and Computational Cost Analysis: The TopoPoint architecture, especially USGN, which includes multiple GCN modules, is conceptually complex and is expected to require significant computational power. It is necessary to analyze whether the performance improvement compared to simpler models justifies the additional computational cost.\n\n**A1**: As you noted, real-time performance is crucial for application deployment. Following your suggestion, we evaluated the model's computational resources on Tesla A100 GPU, with the results as follows:\n\n| Model                | FLOPs  | #PARAMS | FPS  |\n| -------------------- | ------ | ------- | ---- |\n| TopoNet              | 712.1G | 38.6M   | 16.6 |\n| TopoLogic            | 665.0G | 37.8M   | 17.1 |\n| TopoPoint (w/o USGN) | 973.8G | 66.3M   | 15.3 |\n| TopoPoint            | 989.3G | 67.9M   | 14.8 |\n\n- **As shown in the table, GCN does not actually consume significant computational resources, and the introduction of USGN has a negligible impact on FLOPs, #PARAMS, and FPS.** \n- This is because GCN is merely a method for aggregating node information, whose operations consist of simple matrix multiplications with any complex operation, i.e.,  $GCN(X, A) = \\sigma(\\hat{A}XW )$ as described in Line 168-170 in the paper. Furthermore, in the topology reasoning task, the number of nodes involved in GCN is equal to the number of queries ($N_t=100, N_p=200, N_l=300$), which actually has very low computational cost and small number of parameters..\n\n----\n\n**Q2:** Proof of Impact on Downstream Tasks:  If a quantitative evaluation of how much improved topological accuracy actually improves the performance of the planning module had been included, the practical usefulness of the proposed method and the logical persuasiveness of the paper would have been greatly enhanced.\n\n**A2**: Previous methods often suffer from significant endpoint deviations in lane predictions, resulting in non-smooth connections between multi-segment lanes. Directly using such predictions for vehicle planning and control leads to frequent and unnecessary steering maneuvers, necessitating additional post-processing to mitigate these issues. In contrast, our method effectively eliminates endpoint deviations, producing smoothly connected lanes—as evidenced by the visualizations in Figure 4 and the supplementary materials—thereby providing more reliable inputs for downstream planning and control tasks. **Furthermore, experimental results in Table 1 of paper \"*Reasoning multi-agent behavioral topology for interactive autonomous driving*\" demonstrate that improved topological reasoning directly contributes to better performance in downstream planning tasks.**\n\n[1] H. Liu, L. Chen, Y. Qiao, C. Lv, and H. Li, “Reasoning multi-agent behavioral topology for interactive autonomous driving,” in Annual Conference on Neural Information Processing Systems, 2024.\n\n-----\n\n**Q3:** Analysis of Inter-Module Error Propagation: Whether the performance degradation of $DET_t$ only affects the lane-traffic topology $TOP_{lt}$, or whether it can also negatively affect the geometric accuracy of lane $DET_l$ and endpoint $DET_p$ predictions through feature propagation.\n\n**A3:** This is an interesting question, and our answer is almost no.\n\n- From a logical perspective: Traffic and Lane are mutually independent entities, and only the $TOP_{lt}$ metric evaluates the topological quality between them. Thus, the performance of Traffic only affects $DET_t$ and $TOP_{lt}$, but not $DET_l$ or $TOP_{ll}$.\n- From the model methodology: **Traffic and Lane are predicted through separate branches from independent queries, with no interaction at the feature level and no cross-branch influence. The only interaction is that GCN$_{lt}$ in USGN enhances Lane queries using Traffic**. Additionally, end-to-end joint optimization will eventually result in slight influences between components.\n- From the experimental results: According to Table 2, after adding FVScale, $DET_t$ and $TOP_{lt}$ show significant improvements (46.8→53.8, 24.3→27.0), while $DET_l$, $TOP_{ll}$, and $ DET_p$ are slightly affected (29.2→29.4, 23.4→23.8, 44.5→44.8), which verifies their weak correlation of influence. According to Table 3, after adding $GCN_{lt}$, the most affected is $TOP_{lt}$ (27.1→28.8), while the impact on other components is minor (29.8→30.6 on $DET_l$, 54.2→54.5 on $DET_t$, 26.9→27.4 on $TOP_{ll}$).\n\n----\n\n**Q4:** Verification of Generalization Performance on Other Datasets: All experiments in this paper were performed only on the OpenLane-V2 benchmark. To prove the generalizability of the TopoPoint methodology, we would like to know if there are additional experimental results on other tasks in nuScenes or Argoverse2, or on different datasets (e.g., Waymo Open Dataset).\n\n**A4**: Adequate experiments are crucial, and this question can be answer from three aspects:\n\n- **First, OpenLane-v2 actually consists of two independent data subsets: subset_A is built upon Argoverse2, while subset_B is built upon nuScenes. To some extent, already demonstrates its generalization ability.**\n\n- Like other works such as TopoNet and TopoLogic, our research focuses on the topology reasoning task and is not applicable to other tasks in nuScenes or Argoverse2, so we cannot provide evaluations on those other tasks.\n\n- We also hope to have more data for research. Unfortunately, due to the constraints of annotation costs, there are currently no more datasets in the emerging field of topology reasoning. OpenLane-v2 is already the large and widely recognized dataset in this area, and all topology reasoning methods are evaluated on this benchmark. Although the Waymo Open Dataset is also an autonomous driving dataset, it don't providing topology annotations, which makes evaluations impossible.\n\n----\n\n**Q5:** Sensitivity on calibration accuracy: TopoPoint explicitly acknowledges that it relies on accurate calibration, could you provide sensitivity analysis experimental results showing the changes in each evaluation indicator for each correction error range.  This is important information for evaluating the robustness of the model to sensor errors that may occur in actual vehicle applications.\n\n**A5**: As mentioned in our paper, accurate camera calibration is also important for the model. Camera parameters affect the accuracy of BEV features, which in turn impacts the performance of lane detection and endpoint detection. **To evaluate this effect, we introduced noise to the extrinsic parameters of the camera, adding translation and rotation noise with standard deviations of (10 cm, 3°) and (20 cm, 5°)**, respectively, with the experiment results as follows:\n\n| Model | $DET_l$ | $DET_t$ | $TOP_{ll}$ | $TOP_{lt}$ | $OLS$ | $DET_p$ |\n| --------- | -------- | -------- | -------- | -------- | -------- | -------- |\n| TopoPoint | **31.4** | **55.3** | **28.7** | **30.0** | **48.8** | **52.6** |\n| 10 cm, 3° | 30.3 | 55.1 | 27.2 | 28.8 | 47.8 | 50.8 |\n| 20 cm, 5° | 28.6 | 54.8 | 25.5 | 27.0 | 46.5 | 48.7 |\n\n- **It can be observed that as the noise increases, the performance of both lane and endpoint detection degrades accordingly, which further affects the overall topology reasoning. In contrast, traffic element detection remains largely unaffected, as it is performed directly in the front-view image space and does not rely on BEV features.**\n\n- This is a common challenge faced by topology reasoning and even 3D perception, and is not limited to our method.\n\n- While it shows some sensitivity to calibration accuracy, this issue can be effectively addressed via accurate online camera calibration during deployment.\n\n**Q6:**  Is there any reason for mentioning the conference in Table 1.\n\n**A6**:  We included the conference venues in Table 1 to **highlight that the compared methods are representative and state-of-the-art approaches recognized by the research community.** This underscores the rigor of our experimental setup and the competitiveness of our proposed method.\n\n**If you have any further questions or suggestions about our article, we'd love to discuss our content with you.**"}]

Author rebuttal:

Meta-review: The paper focuses on lane topology prediction. It identifies issues in prior work where lanes with the same end point end up with divergent end points, and proposes a novel approach to jointly predicting lanes and end points.

Reviewers agree on the novelty of the approach and the strong results. Questions raised about experimental protocol were addressed by the authors in the rebuttal. As such, I recommend acceptance.
Decision: Accept (poster)