PointRePar : SpatioTemporal Point Relation Parsing for Robust Category-Unified 3D Tracking
Authors: Juntao Liu
Zikun Zhou
Zhuotao Tian
Guangming Lu
Jun Yu
Wenjie Pei
Venue: ICLR 2026 Poster (2026)
Keywords: 3D single object tracking
category-unified
point relation parsing
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: 3D single object tracking (SOT) remains a highly challenging task due to the inherent crux in learning representations from point clouds to effectively capture both spatial shape features and temporal motion features. Most existing methods employ a category-specific optimization paradigm, training the tracking model individually for each object category to enhance tracking performance, albeit at the expense of generalizability across different categories. In this work, we propose a robust category-unified 3D SOT model, referred to as SpatioTemporal Point Relation Parsing model (*PointRePar*), which is capable of joint training across multiple categories while excelling in unified feature learning for both spatial shapes and temporal motions. Specifically, the proposed *PointRePar* captures and parses the latent point relations across both spatial and temporal domains to learn superior shape and motion characteristics for robust tracking. On the one hand, it models the multi-scale spatial point relations using a Mamba-based U-Net architecture with adaptive point-wise feature refinement. On the other hand, it captures both the point-level and box-level temporal relations to exploit the latent motion features. Extensive experiments across three benchmarks demonstrate that our *PointRePar* not only outperforms the existing category-unified 3D SOT methods significantly, but also compares favorably against the state-of-the-art category-specific methods. Codes will be released.

Published as a conference paper at ICLR 2026
PointRePar: SPATIOTEMPORAL POINT RELATION PARSING FOR ROBUST CATEGORY-UNIFIED 3D TRACKING
Juntao Liu", Zikun Zhou', Zhoutao Tian 12, Guangming Lu', Jun Yu', Wenjie Pejl.2
HHarbin Institude of Technology. Shenzhen
Pengghheng Laboratory dottjjieccerrruutleoeoe look. com, tiarchuocaoehitt.euuuu
ABSTRACT
3D single object tracking (SOT) remains highly challenging task due to the in- herent crux in learning representations from point clouds to effectively capture both spatial shape features and temporal motion features. Most existing methods employ calegory-specified optimization paradigm, training the tracking model individually for each object category to enhance tracking performance, albeit at the expense of generalizability across different categories. In this work, we propose a robust category-unified 3D SOT model, referred to as Tempo- ral Point Relation Parsing model (PointRePar), which is capable of joint training across multiple categories while excelling in unified feature learning for both spa- tial shapes and temporal motions. Specifically, the proposed PointRePar captures and parses the latent point relations across both spatial and temporal domains to learn superior shape and motion characteristies for robust tracking. On the one hand, it models the multi-scale spatial point relations using Net architecture with adaptive point-wise feature refinement. On the other hand, it captures both the point-level and box-level temporal relations to exploit the la- tent motion features. Extensive experiments across three benchmarks demonstrate that our PointRePar not only outperforms the existing category uniifed 3D SOT method CU'Track significantly, but also compares favorably against the state-of- the-art category-specifier methods.
caamba-based U-
Comparison of Trackers on Nuseenes
18.40M
70
6.45% Gains
65
9.85M
12 12,2%% Gaina
60
55
Success
1.30M
50
Parameters
45
40
Category Paradigm
Category-uniffed Paradigm
35
P2B
PTTR
CXTrack
GLT-T
M2Track
SegTrac
MBPTrack
SiamMo
SiamCUT
MoCUT
TrackAny3D
PointRePar
Figure 1: Comparisons Success of the NuScenes Caesar et al. 2020) dataset between our method
and state-of-the-arr 3D trackers. Our approach outperforms the existing -anfgory-unified track-
(is et al. 2024 Wang al. 2025) considerably and compares favorably against the category-
specified trackers.
Equul contribution this work. Corresponding authors.

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Published as a conference paper at ICLR 2026
Background Poinls
Foruground Points on Frame T-1
Foroground Points on Frame"
Single Fraie T
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Mor timilar Moree Mecremmtnivee
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Resture on Corsenttive
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PointNet++
AdaForme
PointRePar (Ours)
Figure 2: t-SNE maps of encoded features using PointNet++, AdaFormer and our PointRePar. Our model able to not only learn more discriminative spatial features between foreground and back- ground for single frame (Right Top), but also preserve better temporal feature consistency between two consecutive frames (Right Bottom).
INTRODUCTION
3D single object tracking (SOT) on point cloud data aims to locate target object by predicting its position and pose a streaming way. As fundamental task in 3D vision, has a wide range of applications, including autonomous driving and surveillance systems. Despite the remarkable progress of SOT in the 2D visual domain, 3D SOT remains highly challenging due to the inherent crux in effectively learning representations for both spatial shape and temporal motion, arising from the sparsity and incompleteness of point clouds.
al. 2019 et 2020 Zheng al. 2021: Shan et al. 2021 Nic et al. 20233 Xu et al. 2023a 3 b; Lin et al. 2024 Yang et al. 2024 Zhou et al. 2022 Shan jt al. [022 Liu et al. 2024]] 2025 2023) adopt category-specified training paradigm, where mod- els are trained separately for each category to ease optimization and improve tracking performance. This paradigm allows the model's entire capacity to focus on learning patterns specific sin- gle category, significantly reducing the complexity of model fitting and accelerating optimization convergence. However, this learning paradigm suffers from two potential limitations. First, it im- pedes the tracking model from leveraging and learning the generalizable patterns across categories, thereby limiting its inter-category robustness and generalizability in diverse scenarios. Second, such paradigm is significantly less efficient than the category-uniifiee training paradigm performing joint training across multiple categories.
Most existing methods
Giancola netaoodsGiianolaa
Recent studies have initiated the exploration of the category-unifee training paradigm for the 3D SOT task. A prominent example is CUTrrac et al. 2024), which introduces deformable group- ing vector attention mechanism dynamically adapt to objects of diverse sizes and shapes across categories. Despite the effectiveness of its designs, CU'Track remains significantly inferior to state- of-the-art category-specing
tracking methods, primarily due to its limited feature learning capabil- ities for both spatial shapes and temporal motions. We conducted a systematic analysis of the con- tributing factors underlying the failure of prior category-specifer approaches and CUTrack within the category- -niffidd training paradigm, which can be attributed to the following reasons: 1) As shown in Figure 2, both the PointNet++ backbone conventionally used in the category-speciffed methods and AdaFormer proposed by CUTrack suffer from poor feature space separability between target and background points during single-frame point cloud encoding, leading to confusion with the background. 2) Both backbones exhibit substantial feature discrepancies for the same target across consecutive frames. This significant temporal feature inconsistency impedes robust feature matching. 3) Although CUTrackk unifies the motion patterns of diverse categories into normal
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distribution, it basically lacks adequate temporal modeling abilities and category agnostic design for temporal relations parsing, which further constrains its performance. In this work, we propose robust category-unifin 3D SOT model, named the SeapioTemporal Point Relation Parsing model (PointReParr), which enables joint training across multiple categories while excelling in unified fea- ture learning for both spatial shapes and temporal motions of diverse categories.
aateoory-agoostic
As shown in Figure our method exhibits superior capability for learning both discriminative spatial shape features in single frame and temporally consistent motion features between two consecutive frames. Our contributions can be summarized as follows:
We propose PointRePar, an effective yet efficient category- uniffed 3D SOT framework ca- pable of joint training across multiple categories, delivering excellent tracking performance owing to its sophisticated parsing of spatiotemporal point relations.
We devise a novel Dynamic Feature Aggregation mechanism for point-wise adaptive fea- ture refinement, upon which the specially adapted "U-shaped Spatial Relation Parsing Mamba' is further designed to enable our model to capture intricate multi-scale spatial point relations.
We present a simple yet effective long-term temporal relation parsing scheme, which en- ables our method to capture dual-level category-agnostic features: point-level motion de- tails and box-level trajectory features.
We introduce the 'Conditional Gaussian Perturbation' scheme to simulate historical pre- diction errors under different sparse scenes, thereby enhancing our model's robustness. Extensive experiments on KITTI, NuScenes and WOD demonstrate that our model not only outperforms existing calegory-unified 3D SOT Trackers significantly, but also compares favorably against the state-of-the-a sategory-speccfien 3D SOT methods.
2 RELATED WORK
2.1 3D SINGLE OBJECT TRACKING
Early 3D single object tracking (SOT) approaches/Gianeond al., 2019 Qi et al. 2020 Zheng et al.
2021 (Shan et al. 2021 Nie et al. 2023 Xu et al. 2023a b) inherited the Siamese matching paradigm from 2D SOT. SC3D et al. 2019) utilizes Kalman filtering to heuristically sample target proposals and computes similarity with the given template target, selecting the highest-scoring pro- posal as the predicted result. P2B Qi et al. 2020) integrates SiamRPN with a point-based Region Proposal Network Qi et al., 2019) (RPN) for predicting the target bounding box in 3D point clouds, improving tracking efficiency and performance. Most subsequent studies follow this Siamese match- ing paradigm. (ZTen et al. 202I) introduces box-aware features enhance the correspon- dence between the template points and the target points within the search region. PTT(Shan et al.) 202T) incorporates
Srint-Trac--Transformmee Transfomerr module into the P2B framework to refine the point features. STNet(Hui et al., 2022) designs a coarse-to to-eine correlation network for robust correlation learning. GLT-TQNie et al. 2023 leverages a global-local Transformer voting scheme to generate high-quality 3D proposals. MT'M'racck[Li et al., (023) first leveraged an encoder-decoder structure which exploits continuous historical boxes as motion prior for coarse box autogressive generation, but its computational overhead and model complexity limit practical deployment.
Recently CXTrack Xu et al., 2023a) employs a target-centric transformer to model contextual in- formation. MBPTrack(Xu et al. (023b) designs memory network and box-prior localization network to improve CXTrack. M Track et al. 2022) introduces a motion-centric paradigm, modeling the target motion across two consecutive frames for position estimation. Segtrack3D
ct al. 2024) proposes a Seq2Seq framework that combines Siamese and motion-centric paradigms. BEV Track Yang et al. 2025) PPP al. 2025), .Siammo(Yam et al.) 2024 further models the target motion in a Siamese network, achieving
performance. All above methods em- ploy a category-specified training paradigm. Against this background, CUTrack(Nie et al.) 2024) introduces a category-unified training paradigm for the first time and proposes dynamic grouping vector-atiention as the solution. TrackAny3D(Wan) (Wang et al. 2025) utilizes mixture of geometry experts to learn distinct geometric characteristics across diverse obeject categories. However, CU- Track and Any33 suffer from suboptimal computational efficiency, and both performance lag behind category category-specified methods. Unlike them, we propose category- equipped with mamba, efficient dynamic feature aggregation mechanism and a long-term temporal relation parsing scheme for modeling spatiotemporal point relations robustly. As far we know, PointRePar is the first category-unified 3D tracker that performs favorably against state-of-the-ar category-specde specified trackers.
3D tracker that is
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Coarse Prediction Stage
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Figure 3: An overview of our proposed PointRePar architecture. PointRePar first utilizes a Motion- Based lightweight tracker to predict coarse box in the first stage. In the second stage, PointRePar employs a spatial relation parsing backbone to capture the local and global features of point clouds. After that, the Temporal Scan Mamba and Long-tern long-term Motion Trajectory Rectification are introduced to perform temporal relation parsing on top of the point features and box sequences. Finally, the enhanced features and box sequence are fed into cross-attention decoder predict high-quality boxes.Condiies Conditional Gaussian Perturbation is only used in training phase.
3
METHOD
3.1 OVERVIEW
Problem Definition. Given a 3D bounding box (BBox) indicating the initial state of the target object, the task of 3D single object tracking aims to locate the target object by predicting its 3D BBox B, = (xe, Mt: 2e, 2/r) in a streaming way. Herein, (Iz, Yt, 2t) denote the 3D coordinates of the object time t, and eh refers to its yaw angle. Note that the size of the BBox is assumed to stay unchanged during tracking while the pitch angle and roll angle of the object are always 0, aligning
with established conventions in previous studies(Qi et al. 2020 Zheng al. 2022 Lin al. 2024
Overall framework. Our PointRePar is crafted to capture and parse intricate point-level relations within spatial and temporal domains, aiming to characterize the shape and motion of the target object accurately for category- 3D object tracking. As illustrated in Figure 3 PointRePar adopts a coarse-to-fine tracking framework, in line with methods(Zheng et al., 2022 Lin et al., 2024). Initially, it utilizes lightweight tracker consisting of SegPointNet and miniiPinntNet((1 et al. 2017a) to efficiently provide a coarse prediction of the object BBox. In the subsequent refining stage, the coarse prediction serves as the query to perform fine- ane-ggrinedd decoding through cross- attention, applied to the encoded features enriched with parsed spatiotemporal point relations.
2023) as the fundamental component The proposed PointRePar leverages Mamba Gu Dao
for spatial feature encoding, benefiting from its robust long-term sequence modeling capabilities. Specifically. PointRePar first leverages a point-based backbone equipped with multi-scale Mamba and the Dynamic Feature Aggregation mechanism to conduct more discriminative and temporal consistent features extraction in category-unified training paradigm. The backbone captures unified multi-scale spatial dependencies through specially designed "U-shaped Spatial Relation Parsing Mamba', which hicrarchically parses and fuses spatial relations across scales using a Mamba-based U-Net architecture with DFA modules.
To capture category-agnostic long-term temporal relations, PointRePar not only models the point- level spatiotemporal relations with 'Temporal Scan Mamba'. but it also performs object motion trajectory rectification over the coarse prediction by learning the box-level sequential relations. Fur- thermore, introduce the "Conditional Gaussian Perturbation' scheme, which adds Gaussian noise
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to the target object's historical motion trajectory to simulate prediction emrors, thereby improving the robustness of the model. In the following subsections, we will elaborate on these designs.
3.2 MULTI-SCALE SPATIAL RELATION PARSING
Comprchensive spatial point relation parsing in category- unnifidd training paradigm requires effective modeling of both global and scati features for diverse categories. Therefore, we propose U- shaped Spatial Relation Parsing Mamba (USRPM), inspired by MS MSVMamba(Shir et al. 2024), which balances global spatial modeling and computational efficiency. However, integrating Mamba into point set networks alone fails to sufficiently capture local geometric details adapt diverse target shapes. To address this, we introduce Dynamic Feature Aggregation (DFA), a module that adaptively aggregates local geometric features across variable receptive fields, enabling robust spatial relation parsing for diverse categories. Details are as follows.
U-Shaped Spatial Relation Parsing Mamba. As illustrated in Figure the proposed U-Shaped Spatial Relation Parsing Mamba consists of multiple Mamba blocks with Dynamic Feature Aggre- gation modules and hierarchical downsampling layers. At each level, the point cloud features are processed and progressively downsampled to construct multi-scale representation. We innovatively design weight- Mamba blocks that perform bidirectional scanning during downsampling and upsampling operations. This design not only enables computationally efficient bidirectional model- ing as MSVMamba does, but also ensures cross-scale semantic consistency across divergent sean- ning orientations. The final multiscale feature set J {F*, , Fa8 aggregates the hierarchical context for the tracking task. Please see more details in the Appendix
Dynamic Feature Aggregation. Figure d) illustrates the pipeline of Dynamic Feature Aggre- gation (DFA). Given an input point cloud L.W. E R Nx3 and its associated features {ft E N this module first generates an offset scale Ap: Rx* (confined [0, 1)) and a weight w; E IR xx1 for each point via two MLP layers. These offsets dynamically adjust the positions of the original points, producing N K virtual points with corresponding weights Wo:
P. (pr + Apik Rmax I,.,,,
W = (iigmoid(MLP(A))])
(2)
where Rmax denotes the maximum displacement radius, and K is the number of offsets per point. Next, we interpolate features for each virtual point using inverse distance weighting:
EN E (z) fi EN (x)
with w,(x)) d(x, (x +
where d(z, xz)) measures the L2 distance between x and zj. Finally, the original point features are updated by aggregating the features of their K virtual counterparts:
p LayerNorm ft
wikfix
The DFA module adaptively adjusts the receptive field for each point, enhancing geometric per- ception for different targets of diverse shapes and sizes while mitigating information loss during downsampling. More importantly, it can enhances intra-object feature similarity while simultane- ously boosting feature discriminability against background points within point cloud representations for diverse categories.
3.3 LONG-TERM TEMPORAL RELATION PARSING
The catesory-agnostic trajectory information is critical for handling sparse or occluded point clouds and mitigating drift in cluttered scenes with inter- or intra-class distractors. While previous multi- frame works Lin et al. 2024| Liu et al. 2024) focus on the temporal modeling of point features, they often neglect the explicit motion encoded in BBox sequences. Therefore, method jointly models long-term temporal dynamics for not only point features but also target trajectory while maintaining efficiency through wiggtt mamba-based architecture.
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Point-level motion parsing. For modeling the temporal dynamics based multi-scale features, employ Mamba block to capture frame-wiss wise temporal relations, named as Temporal Scan Mamba, leveraging its strength in long-sequence modeling, shown in Figure Increasing the temporal modeling window for point features can enhance tracking performance, but it typically results in excessively high computational costs.
Box-level motion parsing. Instead, we propose a lightweight Long-term Motion Trajectory Rec- tification (LMTR) module that efficiently encodes long-term box sequences. Specifically, given historical box sequence B,, = [Br- I,, ..., Br-1 (where L > T), we concatenate it with the coarse prediction B, to form B, (B....... B.). Following standard practice(Zheng et al. 2021 we convert boxes to corner representations and use a MLP to project them to tokens Xt I2 augmented with learnable temporal embeddings E RLXC;
- X + E.
(5)
To model temporal dependencies across the box sequence, tokenized representations are processed through Mamba block that effectively captures long-range 1-D sequential patterns. The final output tokens spanning the last I' time steps are aggregated to construct the decoder input query Qin. where each temporal position Y; E IR (88xC' encapsulates frame-specific spatial information. This process is formally defined
Y = Mamba(A (x))
(6)
Qin = CYo, Y -I+1 -I+.... Y-1).
(7)
3.4 CONDITIONAL GAUSSIAN PERTURBATION
To enhance the robustness of the model against detector localization errors, conventional ap- proaches(Zheng al. 2022 Lin al. 2024) inject uniform noise into training trajectories, disregarding the inherent correlation between eтror magnitude and scene complexity, thereby limiting the performance sparse scenes. To address this issue, we first investigate error patterns in real-world detection systems. As shown in Figure 4 we observe that localization uncertainty scales inversely with point cloud density, sparse scenes provide fewer geomet-
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Emconitee
ric cues for accurate target localization.
022
Inspired by the above analysis, pro- pose Conditional Gaussian Perturbation (CGP), which dynamically modulates perturbation in- tensity according to scene sparsity. Con- cretely, given input box B; with spar- sity ratio (measured as the reciprocal of point density within the detection region), our method applies axis-wise perturbations through a sparsity condiity--odiiioonedd noise generator:
Thrget Ponts toree
Figure 4: Illustration of the distribution of pre- diction errors along the X-axis versus the target- to-scene point ratio, defined the ratio between the number of target points and scene points. The localization uncertainty decreases cloud density increases, which implies that sparse scenes offer fewer geometric cues for precise tar- get localization.
85.9,2 = Bz.y.. - (N(00 1),
(8)
point
where 1) denotes standard Gaussian noise, >>>>> controls the exponential decay rate of noise magnitude with increasing density, and Az.g. determines axis-specific scaling factors.
By explicitly correlating noise magnitude with scene density, CGP achieves two critical objectives: 1) amplifying perturbations under sparse observations to mimic error accumulation in low-density regions, and 2) preserving physically consistent noise bounds in dense scenarios. This density- aware perturbation strategy, as demonstrated our experiments, significantly enhances robustness compared to the uniform noise injection.

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Table 1: Performance comparisons with state-of-the the-art methods in terms of Success/Precision
NuScenes and WOD benchmark. " represents models trained in category- paradigm and are trained in category- paradigm.
Method
Car
Pedestrian [33,227]
Truck
Trailer [3,352]
Bus
Mean
Prame Count
[64,159]
[13,587]
[2.953]
[117,278]
AC3D/Giancolle et al.
2019
22.31/21.93
11 2..29/12.65
30.67/27.73
35.28/28.11 12
29.35/24.08
20,70/20.15
P2BlCh et al. 2020
38.81/43.18 18
28. 29/52.24
42,95/41.59
48 96/66/000
32.95/27.44 41
36, 46.48/45.00 08
BAT el al 2021
40.73/43.29
28 83/53.32
45.34/42.58
52.59/44.89 89
35.44/28.01
38. 38.10/45.71
FTTRizhou
51.89/58.61
29. 29.90/45.09
45.30/44.74
45.87/38.36
43.14/37.74
44.50/52.07
MFTracktZZeng
2022
55.85/65.00 09
12/10/2
57.36/59.54
57.61/58.26
51.39/51.44
49.32/62.73
MBPT et al 20236
62.47/70.41
45.32/74.03
68/18/63.31
65.14/61.33
55,41/51.76
57.48/69.88 88
SCVTrrack (hang cl al 2024
58.90/67.70
34.50/61 .50
60.60/61.4 140
59 19.50660.16 10
54,30/53.66 60
52. 70/64,70
SeyTruck3 Linn cl
62.55/71.46
99/94/68.57
60.97/63.04
68.37/61.76
54.33/53.52
55,92/68.0
SiamMo Yong ci
20243
64.85/72.24
46 26/76/66.66
68.22/68.88 81
74.21/70.63
65.63/62.07
60.31/72.68
PointKcPar(Uun
71.29/78.20
48.36/78,98
68.89/71.27
71.57/70.56
56.40/55.26
64.15/76.81
SiamCUT Nie et al.
2024
40.96/44.91
31.42/53,80
53.91/52.65
63.29/58.21
41.03/38.01
40.41/48.54
McCUT (Ne et al. 2024
57.32/66.01
33.47/63.12 12
61.75/64.38
60.90/61.88 84
57. 57.39/56.07
51.19/64.63
TrackAny3D (wang at 2025
59. 39.30/66.46
40.37/68.70
62.70/62.80
66.12/59.20
61.01/58.02
54.57/66.25
Point
72.95/80.70
49.86/79.55
75.34/77.82
75.87/73.75
72.82/72.80
66.76/79.64
Table 2:
Performance comparisons with tfate-of-the-art methods in terms of Success/rrecision on
KITTI dataset." represents models trained in category-unified paradigm and are trained in
category-specified paradigm.
Method
Car
Pedestrian [6.088]
Van
Cyclist [308]
Mean
Hardware
Fps
Frame Count
[6,424]
[1.248]
[14,068]
MC3D(Giancola et al
2019
41.3/57.9
18.2/37.8
40.4/47.0
41 41.5/70.4
31.2/48.5
GTX 1080Ti
2
P et al. 2020
56.2/72.8
28.7/49.6
40.8/48.4
32. 32.1/44.7
42.4/60.0
GTX 1080Ti
20
BATTZheng ct al. 2021
60.5/77.7
45.7/72.5
52.4/67.0
33.7/45.4
51.2/72.8
RTX 2080
57
PTTR Zhou et
65.2/77.4
50.9/81.6
52.5/61.8
65.1/90.5
57.9/78.2
Tesla V100
50
OFTracktZhen el 2022
65.5/80.8
61.5/88.2
53.8/70.7
73.2/93.5
62.9/83.4
Tesla V100
57
CXTrack Xu et al. 202.5
1/816
67.0/91.5
60.0/71.8
74.2/94.3
67.5/85.3
RTX 3090
29
MBPTrack(XL Xu ct al. 20256
73.4/84.8
68.6/93,9
61.3/72.7
76.7/94.3
70.3/87.9
RTX 3090
50
SyncTrack eM al: 2023
73.3/85.0
$4.7/80.5
60.3/70.0
73.1/93,8
64.1/81.9
TITAN RTX
45
SCVTrack Zhang al 2024
68.7/81.9
62.0/89.1
58.6/72.8
77.4/94.4
65.1/84.5
RTX 3090
31
M3SOT
Lau el al. 2024
75.9/87
66.6/92.5
59.4/74.7
70.3/93.4
70.3/88.6
RTX 3090
38
SiamMo Yang al. 2024
7
68.6/93.9
67.9/80.5
78.5/94.8
72.3/90.1
RTX 4090
108
SiamCUT NNe et al.
2024
58.1/73.9
48.2/76.2
63.1/74.9
36.7/47.4
54.0/74.6
RTX 30701I
36
MoCUT el al.
2024
67.6/80.5
63.3/90.0
64.5/78.8
76.7/94.2
65.8/85.0
RTX 3070TI
48
TrackAnyaD(Waan ct al. 2025
73.4/85.2
59.6/85.6
70.0/82.8
74.7/94.0
67.1/85.4
RTX 3090
28
Pointkeran(Uurs)
76.7/87.5
67.A/91.9
69.4/82.2
74.9/94.1
72.0/89.1
RTX 3090
37
4 EXPERIMENT
4.1 EXPERIMENTAL SETTINGS
Datasets. To validate the effectiveness of our method, we conduct evaluations on three popular datasets, namely KITTI Dataset (WoD)(Sun et al. 2020). KITTI contains 21 point cloud video sequences with ground- truth labels, spanning 8 object categories. We split the dataset into training, validation, and testing folds following the same setup as previous works(Nic et al. 2024). NuScenes is more challenging dataset with a large number of sparse scenes. comprises 700, 150. and 150 scenes for training, validation, and testing, respectively. Following previous works including LiDAR -aPPaag et al.) 2021) and CUTrack Nie et al. 2024), we use the WOD dataset solely to evaluate the pretrained models on the KITTI dataset. Specifically, the test set of WOD consists of 1121 tracklets, divided into easy, medium, and hard subsets based on the sparsity of the point clouds.
al. (012)) NuScenes(Caesar
al., 2020) and Waymo Open
Evaluation Metrics Following the previous Wir et al., 2024 Lin et al. 2024). we use Success and Precision metrics in One Pass Evaluation manner to evaluate our method. Success denotes the Area Under Curve(AUC) for the plot showing the ratio of frames whose intersection over (IO))(OOU) between the ground truth and the predicted BBox is greater than a predefined threshold. Precision is defined the AUC in terms of distance between the center of two boxes.
Implementation Details. Our backbone contains three set abstraction (SA) layers of PointNet++ et al. 2017b) for downsampling, with receptive radius of 0.3, 0.5, and 0.7 meters respectively. We
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Penlstrian Car
Timeline
Ground Truth
SiamMo
PointitePar (Ours)
Figure Qualitative comparison between our model and the state-of-the-are category-specife method SiamMo on 3 challenging samples with sparse point clouds across categories.
train our model on NVIDIA XTX-3090 GPUs using Adam with batch size of 64. More training details can referred to Appendix
4.2 COMPARISON WITH STATE-OF-THE-AN METHODS
Results on NuScenes. Table ] presents the experimental results on NuScenes dataset containing challenging sparse scenes. It can be observed that our model significantly outperforms MoCUT across all categories, achieving substantial performance improvement Mean Success and Precision, which demonstrates the superiority of our model. Furthermore, com- pared to previous state-of-the-art category tracking methods, our method also establishes remarkable performance superiority. Moreover, The category-specifen PoinRePar still significantly surpasses SiamMo overall, particularly in the large-data categories like "Car", "Pedestrian" and "Truck". Our PointRePar is designed for learning generalizable knowledge, which endows it with distinct performance superiority over SiamMo on large-data categories.
15.57/15.01 in terms of
Results on KITTI. We compare our proposed PointRePar with both category methods and category- uniifed methods on the KITTI dataset, which are shown in Table - Our method surpasses the newest category-unin unniied method MoCUT on most categories, leading to a substantial improvement in 'Mean' performance of both Success and Precision. Furthermore, it is worth noting that our method outperforms most of category-specifier methods, only slightly inferior to SiamMo in terms of 'Mean' performance, which is quite encouraging.
Robustness Comparison on Diverse Sparse Scenes.
KITT (L15)
Nox (b:15)
To verify the robustness of PointRePur across different sparsity scenes, compare PointRePar with the state-of-the-art method SiamMo in Figure 6, evaluating their perfor- mance across different datasets and sparsity conditions. The experiments demonstrate that PointRePar achieves overwhelming advantages on the NuScenes dataset and outperforms SiamMo in extremely sparse scenes where the number of target points 30 on the KITTI dataset, proving the superior robustness of our method compared category-specifie approaches. Appendix E.2 also provides qualitative results in extreme sparse scenes.
New [15,20)
KITTE (MLAS)
Nux. [30,55)
KITTI (45,00)
Nus. (A5,00)
KIVTEIBUUSS
Nus, (60.55)
KIVTI
Nta 175.90
KITTL (ALI05)
Nis (90,105)
XITTI (105.44)
SiamMo
PointRePar
Figure 6: Performance comparison of PointRePar and SiamMo in Success/Precision across varying sparsity levels (represented by different intervals [i, j) of point numbers) on KITTI and NuScenes. "Nus.' denotes sparse scenes from the NuScenes.
Results on WOD. To validate the generaliza- tion ability of our proposed method across dif- follow previ-
ferent application scenarios, ous (Prk et al.] 2002] Nie al. (2024) and conduct evaluation by applying the models
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Table 3: Ablation studies of four essential technique designs. We ablate each of these techniques from our PointRePar and evaluate the tracking performance on KITTI dataset. DFA denotes Dy- namic Feature Aggregation. USRPM denotes U-Shaped Spatial Relation Parsing Mamba. LMTR is Long-term Motion Trajectory Rectification. CGP refers to Conditional Gaussian Perturbation.
Car
Pedestrian
Van
Cyelist [308]
Mean
DFA USRPM
LMTR
CGP
[6,424]
[6,088]
[1,248]
[14,068]
71.5/85.1
60.8/87.2
59.8/72.7
74.4/94.7
65.7/84.6
75.4/86.0
61.8/89.1
69.3/82.2
74.2/94.4
68.9/87.2
76.0/86.3
63.0/90.5
70.7/83.6
77.7/95.1
69.9/88.1
76.3/86.3
60.1/87.3
69.0/82.6
74.9/94.0
68.6/86.6
7/.8/85.9
62.0/88.6
68 68.6/82.3
75.5/94.2
68.7/87.0
76.7/87.9
62.1/89.8
69.7/82.9
76.5/94.5
69.8/88.4
76.7/88.0
63.6/89.6
69.1/81.8
76.7/94.6
70.4/88.3
76.7/87.5
67.4/91.9
69.4/82.2
74.9/94.1
72.0/89.1
Table Effect the number of offset points the Dynamic Feature Aggregation on four object categories of KITTI dataset.
Number
Car
Pedestrian
Van
Cyclist
Mean
1(urs)
76.7/87.5
67 17.9999.9
69.4/82.2 74.9/94.1
72.0/89.1
2
75.5/86.4
66. 1/90,8
67.8/80.8
74.4/94.3
70.7/88.0
76.3/87.1
67.5/91.6
68,9/81.9 74.4/94.2
71 71.8/88.7
pre-trained on the KITTI dataset to the Waymo Open Dataset (WOD). Our method not only sur- passes all other methods in both category-specified and category-unified methods (+1.7% and +4.0%/49.77 9.7% respectively), but also showcases great generalization ability to unseen scenarios. More detailed results can be referred to Appendix D.I
Inference Speed. PointRePar achieves inference speed of 36.6 FPS when rumning a single NVIDIA 3090 GPU, which is on par with existing multi-frame methods (c.g. SeqTrack3D), Please refer to the Appendix D.8 for more efficiency details and comparison.
Qualitative Comparison. Figure S visualizes the tracking results of our PointRePar and the state- of-the-art category-specifie, method SiamMo on challenging samples with sparse incomplete point clouds across different categories. shows that SiamMo fails to track the target in these challenging scenes and is prone be misled by intra-class distractors, while PointRePr is able to track the target object consistently and accurately, demonstrating the robustness and reliability of our method in challenging scenarios.
4.3 ABLATION STUDIES
Functionality of essential designs. In this set of experiments, we conduct ablation studies to in- vestigate the functionality of the essential designs of PointRePar. As shown in Table 3 we ablate each of four core designs from our PointRePar and evaluate the tracking performance on the KITTI dataset. The results show that all four proposed techniques distinctly enhance the tracking perfor- mance, demonstrating their effectiveness. Both the proposed DFA and USRPM benefit most of the object categories as they are designed to capture the spatial point relations and refine shape fea- tures.It is noted that without USRPM means that USRPM is replaced with PointNet++. The removal of either one would lead to a significant performance degradation. The proposed CGP is particu- larly effective for the "Pedestrian' category, whose motion trajectory is harder to predict than other categories. This is reasonable, as CGP enhances the model's 's robustness by simulating historical prediction errors. Similarly, LMTR has positive effect on the 'Car' category which exhibit more variable trajectories.
Effect of the number of offset points involved in DEA. To explore the effect of the number of offset points in DFA, we report the results in Table g Optimal performance achieved at K=1. Further increasing the number of offset points does not yield additional performance improvements,

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100
80
Precision
60
40
(0,15)
(15,30)
(30,45)
(45,60)
(60,75)
(75,90)
[90,105)
[105,0)
Number target points search frame
Figure 7: Performance comparison of different backbones on diverse sparse scenes of KITTI.
Table 5: Effect of box sequence length involved in the Long-term Motion Trajectory Rectification
(LMTR) on four object categories of KITTI dataset.
Length
Car
Pedestrian
Van
Cyclist
Mcan
73.2/84.4
64.7/91.0
68.1/80.6
73.2/94.1
69.1/87.1
75.5/86.5
66.4/91.7
63.1/74.4
75.8/94.3
70.5/87.9
7(urs))
76.7/87.5
67.4/91.9
69.4/82.2
74.9/94.1
72.0/89.1
10
75.2/85.7
66,5/92.2
69,0/82.5
75.0/94.1
70.9/88.4 4
15
74.4/85.9
64.9/88.8
67.0/79.4
74.1/94.2
68.3/86.8
potentially indicating that single offset point proves sufficient to capture the local geometric context due to the inherent sparsity and singular target nature of 3D SOT point cloud scenarios.
Effeet of USRPM. To explore the robustness of USRPM in sparse scenes, we report the perfor- mance of different backbones on diverse sparse scenes of KITTI in Figure Our USRPM performs favorably against the other backbones in most sparse scenes with minimal computational overhead introduced, particularly in extremely sparse scenes where the number of target points < 15, which validates the obvious advantage of USRPM when dealing with sparse scenes. More ablation exper- iments of USRPM can be referred to Appendix D.4
Effect of the box sequence length involved in LMTR. LMTR is designed to perform object motion trajectory rectification by modeling the box-level sequential relations. We conduct experiments to investigate the effect of the length of box sequence in LMTR on the tracking performance of different categories, set as a hyper-parameter. The results Tab. show that the overall performance across all categories improves as the box sequence length increases, reaching an optimal value when the length is equal 7. Further increasing the sequence length results performance degradation, potentially indicating that the sequence length has exceeded the trajectory pattern length and is negatively interfering with pattern learning.
5 CONCLUSION
In this work, we introduce PointRePar, a robust category-unified 3D SOT model. In contrast to typ- ical category-specifee 3D SOT methods, our PointRePar is capable of joint training across multiple categories to boost the training efficiency substantially while achieving comparable performance to them. PointRePar models multi-scale spatial point relations through globally adaptive feature refine- ment and hierarchical feature parsing, while simultaneously learning long-term motion features by capturing both point-level and box-level temporal relations. Leveraging these essential designs. method outperforms both antegory-specifien and category-unifia 3D SOT methods, demonstrating particular robustness in challenging scenes with sparse point clouds.
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ACKNOWLEDGEMENTS
This work was supported in part by the National Natural Science Foundation of China (Grant No. 62372133, 62125201 and U24B20174), in part by the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2025A1515010705. 2025A 1546), and in part by the Shenzhen Sci- ence and Technology Program (Grant No. 01112022081110500000 ZDCY20250901 13300000..
TTT12002400310200111
REFERENCES
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11621-11631, 2020.
Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition, pp. 3354-33611 IEEE, 2012.
Silvio Giancola, Jesus Zarzar, and Bernard Ghanem. Leveraging shape completion for 3d siamese tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1368, 2019.
Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.007555 2023.
Xu Han, Yuan Tang, Zhaoxuan Wang, and Xianzhi Li. Mamba3d: Enhancing local features for 3d point cloud analysis via state space model. In Proceedings of the 32nd ACM International Conference on Multimedia, pp. 4995-5004, 2024.
Le Hui, Lingpeng Wang, Linghua Tang, Kaihao Lan, Jin Xie, and Jian Yang. 3d siamese transformer network for single object tracking on point clouds. In European conference on computer vision, pp. 293-310. Springer, 2022.
Diederik P Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv: 2014.
Zhiheng Li, Yu Lin, Yubo Cui, Shuo Li, and Zheng Fang. Motion-to-matchingg: mixed paradigm for 3d single object tracking. IEEE Robotics and Automation Letters, 2023.
Dingkang Liang, Xin Zhou, Wei Xu, Xingkui Zhu, Zhikang Zou, Xiaoqing Ye, Xiao Tan, and Xiang Bai. Pointmamba: A simple state space model for point cloud analysis. Advances in Neural Information Processing Systems, 2024.
Yu Lin, Zhiheng Li, Yubo Cui, and Zheng Fang. Seqtrack3d: Exploring sequence information for robust point cloud tracking. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 6959-6963 2024. doi: N0.1109/CCRA57147.2222 1144110613333
Jiaming Liu, Yue Wu, Maoguo Gong, Qiguang Miao, Wenping Ma. and Fei Xie. Instance-guided point cloud single object tracking with inception transformer. IEEE Transactions on Instrumen- tation and Measurement, 72:1-12, 2023.
Jamming Liu, Yue Wu, Maoguo Gong, Qiguang Miao, Wenping Ma. Cai Xu, and Can Qin. MBsot: Multi-frame, multi-field, multi-space 3d single object tracking. In Proceedings of the AAAI Con- ference Artificial Intelligence, volume 38, pp. 3630-3638. 2024.
Jaming Liu, Yue Wu, Qiguang Miao, Maoguo Gong, and Linghe Kong. Revisiting clamese-basee 3d single object tracking with a versatile transformer. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 47(9): (19))8148. 11681 2025.
Teli Ma. Mengmeng Wang, Jimin Xiao, Huifeng Wu, and Yong Liu. Synchronize feature extracting and matching: A single branch framework for 3d object tracking. In Proceedings the IEEE/CVF International Conference Computer Vision, pp. 9953-99633 2023.

---

Published as a conference paper at ICLR 2026
Jiahao Nie, Zhiwei He, Yuxiang Yang. Mingyu Gao, and Jing Zhang. Git-t: Global-local transformer voting for 3d single object tracking in point clouds. In proceedings of the AAAI conference on artificial intelligence, volume 37, pp. 1957-1965, 2023.
Jiahao Nie, Zhiwei He, Xudong Lv, Xueyi Zhou, Dong- Kyu Chae, and Fei Xie. Towards category unification of 3d single object tracking on point clouds. In The Twelfth International Confer- ence on Learning Representations, 2024. http::// httph:htppppppt http///pppeevveeeeee /
net. fatt/forrse forumiid-
@1qcXrzzDD
Jiahao Nie, Fei Xie, Sifan Zhou, Xueyi Zhou, Dong-Kyu Chae, and Zhiwei He. P2p: Part-to-part motion guide strong tracking framework for lidar point clouds. International Journal of Computer Vision, pp. 1-17, 2025.
Ziqi Pang, Zhichao Li, and Naiyan Wang. Model-free vehicle tracking and state estimation in point cloud sequences. In 2021 IEEE/RSJ International Conference Intelligent Robots and Systems (IROS), pp. 8075-8082. IEEE, 2021.
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 652-660, 2017a.
Charles Qi, Or Litany, Kaiming He, and Leonidas Guibas. Deep hough voting for 3d object detection in point clouds. In proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9277-9286, 2019.
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical fea- ture learning point sets in a metric space. Advances in neural information processing systems, 30, 2017b.
Haozhe Qi, Chen Feng, Zhiguo Cao, Feng Zhao, and Yang Xiao. P2b: object tracking in point clouds. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6329 6329-6338, 2020.
Point-to-box network for 3d
Jiayao Shan, Sifan Zhou, Zheng Fang. and Yubo Cui. Ptt: single object tracking in point clouds. In 2021 JEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1310-1316. IEEE, 2021.
module for 3d
Yuheng Shi, Minjing Dong, and Chang Xu. Multi-scale VMamba: Hierarchy in hierarchy visual state space model. In The eiihth Anmual Conference on Neural Information Processing Systems, 2024. URL htt.:tttt///peeeeeeet
wtt.//nt///orteee wtwtorurmmiirrrrr id=r JUOPDCM
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for au- tonomous driving: Waymo open dataset. In Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, pp. 2444-24544 2020.
Mengmeng Wang, Haonan Wang, Yulong Li, Xiangjie Kong, Jiaxin Du, Guojiang Shen, and Feng Xia. Trackany3d: Transferring pretrained 3d models for category-unifiee 3d point cloud tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2025.
Tian-Xing Xu, Yuan-Chen Guo, Yu-Kun Lai, and Song-Hai Zhang. Cxtrack: Improving 3d point cloud tracking with contextual information. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1084-1093, 2023a.
Tian-Xing Xu, Yuan-Chon Chen Guo, Yu-Kun Lai, and Song-Hai Zhang. Mbptrack: Improving 3d point cloud tracking with memory networks and box priors. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pp. 9912-9 2023b.
Yuxiang Yang, Yingqi Deng, Jing Zhang, Hongjic Gu, and Zhekang Dong. Siammo: Siamese motion-centric 3d object tracking. arXiv preprint arXiv:2408 01688, 2024.
12

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Published as a conference paper at ICLR 2026
Yuxiang Yang, Yingqi Deng, Mian Pan, Zheng-Jun Zha, and Jing Zhang. Bevtrack: A simple and strong baseline for 3d single object tracking in bird's-eye view. In 34rd International Joins Conference on Artificial Intelligence, IJCAI 2025. International Joint Conferences on Artificial Intelligence, 2025.
Jingwen Zhang, Zikun Zhou, Guangming Lu, Jiandong Tian, and Wenjie Pei. Robust 3d track- ing with quality-ave aware shape completion. In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 7160-7168, 2024.
Chaoda Zheng, Xu Yan, Jiantao Gao, Weibing Zhao, Wei Zhang, Zhen Li, and Shuguang Cui. Box- aware feature enhancement for single object tracking on point clouds. In Proceedings of the IEEE/CVF International Conference Computer Vision, pp. 13199-13209 2021.
Chaoda Zheng, Xu Yan, Haiming Zhang, Baoyuan Wang, Shenghui Cheng, Shuguang Cui, and Zhen Li. Beyond 3d siamese tracking: A motion-centric paradigm for 3d single object tracking in point clouds. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 11210, 2022.
Changqing Zhou, Zhipeng Luo, Yueru Luo, Tianrui Liu, Liang Pan, Zhongang Cai, Haiyu Zhao, and Shijian Lu. Pttr: Relational point cloud object tracking with transformer. Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 8531-8540, 2022.
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APPENDIX
A DECLARATION OF LLM USAGE
In this study, the Large Language Model (LLM) was employed solely as supplementary tool for the following limited purposes: Text Polishing: To enhance the fluency and academic formality of expressions without altering original content; Grammar Checking: To identify basic grammatical emrors (e.g.. tense consistency, subject-verb agreement), excluding logical or terminological modi- fications. The LLM did not participate in research design, data analysis, conclusion derivation. Authors retained full control over all academic content.
B MORE METHOD DETAILS
U-shaped Spatial Relation Parsing Mamba
Mamba Forward
W Weight Shared
Sampling Mamba Backward
Down Sampling
Mamba Forward
W eight Shared
Up Sampling
DFA
Mamba Backward
Down Sampling
Mamba Forward
DownSampling
UpSmmpling
Figure 8: Details of USRPM. It consists of downsampling path (left branch) and upsampling path (right branch), outputting multiscale feature which preserves fine-grained spatial details while maintaining semantic consistency.
B.1 U-SHAPED SPATIAL RELATION PARSING MAMBA
As depicted in Figure 3b) and Figure & USRPM consists of downsampling path (left branch) and upsampling path (right branch). Given input point cloud P; 1R and its corresponding features F. N XC at level i, the downsampling path (left branch) computes the smaller-scale (low-resolution) features Fi-1 and the downsampled points pi+ as follows:
F = Mamba (1) (Fiwa))
F pi+1 SA(DFA(Fr (i
where Mamba ( denotes the i-th Mamba block, SA(.) is the set abstraction layer from PointNet++ acting as a downsampling operator, and (FA dynamically aggregates local geometric features. Here, Fl Fo serves as the initial feature input.
In the upsampling path (right branch), high-resolution features are progressively reconstructed by integrating lower-resolution contextual information through skip connections, thereby preserving fine-grained spatial details while maintaining semantic consistency.
For points P R N.x3 and features Fiva E RR N.XC level i, apply the same Mamba block (with shared parameters from the downsampling path) in reverse order. By this way, achieves knowledge sharing across different scales while maintaining semantic consistency in different order scanning.
pisl Mamba
(8)
Fiwd
(10)
where pi+1 E M denotes the updated feature after reverse scanning. For restoring high- resolution features in the upsampling path, we propagate the updated point features from N,41
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points to N₂ points with distance-basse interpolation method, where N, and N:+1 (N++1=Nv/2) point set size of input and output of i-th downsampling level. The interpolated features Fip E R N; xC computed as:
Fi Interpolate (Fack pi+ ,
(11)
where Interpolate(-) denotes inverse distancce-wiihhne feature propagation from pi+1 to Pt. These features are then concatenated with the skip-connected forward features Find E IRN. from the downsampling path and processed through an MLP to refine the fused representation:
F' ALL((CnnattFip: Fiwal).
(12)
At the smallest scale, where coarser features exist, we set F = Find The final multiscale feature set J {F), F2, F$} aggregates hierarchical context for the tracking task.
C
MORE IMPLEMENTATION DETAILS
C.I INPUT DETAILS
Since SOT only takes care of one target in a scene, the model only needs to consider the subregion in the entire scene where the tracking target may appear for efficiency. Specifically, we scale up the target BBox at (t-1) timestamp by meters to obtain subregions in KITTI and 4 meters in NuScenes. We then sample 1024 points respectively within these subregions to generate the input point clouds. Following Seqtrack3D et al. (202)), we set time window length to 4 frames, including 3 historical frames and search frame.
C.2 TRAINING DETAILS
We train our model using the Kingma) 2014) optimizer with an initial learning rate of 0.0001. The epoch size is set to 100 for KITTI and 40 for NuScenes. In our experimental configuration, the USRPM consists of 3 blocks and each block contains DFA module and SSM layer with hidden state dimension of 32. For Temporal Scan Mamba and LMTR, we adopt 3-layer Mamba block with hidden state dimension of 16. In addition, the scale factors B and exponential decay rate brought by CGP are set to 0,4,0,300.2 and 1.5, respectively, considering the training noise distribution of previous methods under normal scenes and appropriately choosing c to enhance noise in sparse scenes.
D MORE EXPERIMENTAL RESULTS
D.1 RESULTS ON WAYMO OPEN DATASET
To validate the generalization ability of our proposed method across different application scenarios, we follow previous work(Pang et al. 2021 Nie al. 2024) and conduct an evaluation by applying the models pre-trained on the KITTI dataset to the Waymo Open Dataset (WOD) et al. 2020). The experimental results on WOD are shown in Table 6 We make two notable observations. First, method consistently surpasses all other methods in both category-specifie and category- uniffed training paradigms in terms of the mean performance of all subsets, which showcases the advan- tages of method. Second, both our method and MoCUT outperform all tracking method in the category-speciff training paradigm, which reveals the benefits of the mategory-unifice training paradigm enhancing the model's generalization ability.
Table 6: Performance comparisons with state-of-the-art methods in terms of Success/Precision on Waymo Open Dataset (WOD)(Sun et al.) 2020). " represents models trained in category-unified paradigm and are trained in category category-speciffed paradigm.
Method
Easy
Medium
Vebicle
Easy
Medium [82,253]
Pedestrian
Hard
Mean
Frame Count
[67,832]
[61_252]
[56.647]
[185,731]
[85.280]
174,219]
[241.752]
427,483
P2IV 2020
1/65.4
52.0V50.7
47.9/58.5
52.666177
1/3003
17.8/30.8
17.7/29.3
33.0443.8
BAT Zheng et at 2021
61.0/68.3
53.3/60.9
48.9/57.8
54,7/62.7
19.3/32.6
17 8/.8/2
17.2/28.3
18.2/30.3
33.0443.8
CXTRaa
63.9/71.1
54.2/62.7
52.1163.7
1761
35.4/55.3
29.7/47.9
26.3/44.4
30.7/49.4
33.0443.
TrTrack Zhene et al 2012
68.1/75.3
58.6666.6
45.4644
6/.1/
35 51.4/
30.7/48.4
29.3/45.9
32.0/49.7
44 64.6/58.2
SiamMoi
6/73/
58.1/67.9
56.6168.7
60.677.1
44 8/66.2
6/56.8
31.2/518
37.5/58.6
47 5/63.6
SammCUTANe et Mi24
38.366.0
50.8/6088
49.2/59.
$3.0662.2
13.4/66.6
20.1/32.0
21.4/31.5
21.9133.
35. 4/44.
MoCUT Sie at
68.3/5.00
59.4/66.9
57.1/66.3
61.9/69.7
36.5/54.8
30.8/48.9
29.5/45.4
32.4/49.9
45. 23.2558.5
Peint (Ouis)
67.3/78.0
58.3/70.2
56.0770.3
60.9/73.1
45.8/70.8
36.5/58.9
35.1/58.0
40.1/64.4
49.2/68.2
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D.2 PERFORMANCE CONFIDENCE INTERVALS
As shown in the Table we add a comparison of IOU and center error 95% confidence intervals. The performance confidence intervals of SiamMo Yang al. 2024) and PointRePar largerly overlap, which proves that our method is comparable to SOTA calegory-speciife method on KITTI dataset.
Table 7: Confidence intervals of IOU and Center Error on KITTI dataset. IOU refers to the average intersection over union (IOU) between the predicted BBoxes and the ground truth BBoxes. Center Error refers to the average distance between the centers of the two BBoxes.
Method
IOU
Center Error]
SiamMo
0.712+0,003 0.719#0.003
0.423+0. 028 0.428+0.029
PointRcPar(Ours)
D.3 THE PERFORMANCE GAP BETWEEN THE COARSE STAGE AND THE REFINE STAGE
As shown in Table 9 and Table 8 the results reveal that: (I) The distinct performance gap of PoinRepar (Mean Succ./Prec.) between the coarse and refined stages on both datasets indicates the effectiveness of the refined stage. (2) The coarse stage of our PointRePar significantly outper- forms the coarse stage the baseline model, which benefits from the holistic optimization strategy of both the coarse and refined stages through back-propagation.
Table 8: Performance comparison of coarse-stage and refined stage NuScenes dataset. Success / Precision are reported.
Method
Stage
Car
Ped.
Truck
Trailer
Bus
Mean
Coarse
71.13/79.13 72.95/80.70
48.20/79.09
74.13/76.81
74.95/73.20
71.64/7161
65 65.22/78.49
PointRePar
Refine
49.86/79.55
75.34/77.82
75.87/73.75
72.82/72.80
66.76/79.66
Gap
1.82/1.57
1.66/0.46
1.21/1.01
0.92/0.55
1.18/1.19
1.54/1.15
Table 9: Performance comparison of coarse-stage and refined stage KITTI dataset. Success / Precision are reported.
Method
Stage
Car
Ped.
Van
Cyc.
Mcan
Baseline
Coarse
69.9/83.2
59.1/86.5
58.8/71.9
71.5/93.9
64.2/83.9
Coarse
75.0/86.4
64.5/90.9
18.1/81.1
74.1/93.4
69.8/88.0
PointRePar
Refine
76.7/87.5
67.4/91.9
69.4/82.2
74.9/94.1
72.0/89.
Gap 1.7/1.1
2.9/1.0
1.3/1.1
0.8/0.7
2.2/1.1
D.4 EFFECT OF DIFFERENT DESIGNS IN USRPM
To improve temporal feature consistency between consecutive frames, we introduce weight-shared Mamba blocks that perform bidirectional scanning during downsampling and upsampling opera- tions. This design not only enables computationally efficient bidirectional modeling as MSVManba does, but also ensures cross-scale semantic consistency across divergent scanning orientations. Here, we provide more ablation results to prove the effectiveness of these designs.
Effect of upsampling path. Employing upsampling aims to propagate high-level semantic informa- tion to low-level features. This assists the model in better leveraging fine-gre fne-grained features imbued with advanced semantics, thereby benefiting box estimation. As shown the Table I0 the model without upsampling performs worse in pedestrians, which require richer fine- fine-graine grained information. Furthermore, the upsampling enables more efficient implementation of bidirectional modeling in Mamba. Employing upsampling would not introduce excessive computational overhead.
Effeet of weight-shared Mamba. Our USRPM are designed for learning unified multi-scale global feature of diverse categories. Without weight-shared design, the model fails to achieve knowledge sharing across different scale Mamba block, inducing semantic discrepancies across multi-scale features. As shown in Table 10, our weight-shan design demonstrates superior performance.
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Table 10: Ablation study of different designs in USRPM. Success Precision reported.
Car
Ped.
Van
Cyc.
Mean
USRPM
76.7/87.5
67.4/91.9
69.4/82.2
74,9/94,
72.0/89.1
w/o Upsampling
76.5/87.4
64.2/89.0
07.8/80.6
75,4/94.2
70.4/87.6
w/o Woight-shared
74.6/85.6
63.1/89.7
68.3/80.4
71.7/93.7
69.0/87.1
Table 11: Mean average similarity of target features across consecutive frames and between target- background pairs at different scale.
Target Target
Target to Background
Backbone F
F2
F3
F'
F²
F3
Performance
AdaFormer
0.6283
0,4589
0.2828
0.3135
0.6426
0.8853
67. 67.0/86.4
0.6660
0.5750
0.6131
0,4043
0.5033
0,4810
69.6/87.9
PointRcPar-Mamba
0.6775
0.6244
0.6342
0.3335
0.3713
0.4108
72.0/89.1
D.5 QUANTITATIVE ANALYSIS OF USRPM'S SPATIOTEMPORAN MODELING
Figure demonstrates qualitative results that preliminarily support the hypothesis of PointRePar's effectiveness. To further validate this hypothesis and illustrate how Mamba contributes to the point relation parsing process, we compare our approach with two other attention- backbones in terms of point feature's spatial discriminability and temporal consistency.
AverSim(F1, (F.
Cosine fi),
where ME feFf, E F
n.
meanAver background
Sim FR),
(13)
T. 4=1
1
Σ
AverSim(FF
L
where Fo is the point features of target object and FB is the point features of background, and FO the point features of target object at timestamp t.
We employ Formula D.5 to calculate the similarity between the features of target points and back- ground points, as well as between the features of the target in the current frame and the previous frame, at each layer of the backbone network. As shown in Table 11 USRPM demonstrates higher target feature similarity across two consecutive frames, indicating its superior capability in model- ing temporal consistency. Additionally, USRPM exhibits lower suggetbacckrounde feature similarity, proving its ability to extract features with stronger spatial discriminability. The quantitative results shown in Table 11 demonstrate that method achieves superior spatial discriminability and tem- poral consistency.
D.6 ADAFORMER VS USRPM
For fair comparison, we substitute USRPM with AdaFormer in our framework. As shown in Table 12, the experiments results shows that our USRPM more powerful and efficient. As ev- idenced in the Table, PointNet++ and AdaFormer proposed by CUTrack(Nie et al.) 2024) only achieve 38.2/48.0 and 43.2/52.4. respectively, in sparse scenes where the number of points < 15, greatly constraining their overall performance. In contrast, our USRPM performs favorably against the other backbones in extremely sparse scenes with minimal computational overhead introduced. TTis results also explain why method outperforms others NuScenes by large margin: its
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enhanced robustness in sparse scenes is critical, NuScenes (using 32-beam LiDAR) contains 69% extremely sparse point clouds where the number of points < 15, which is significantly more than conventional datasets like KITTI (64-beam LiDAR).
Table 12: Quantitative comparison of different backbones in diverse sparse scenes of KITTI. Success / Precision are reported.
Backbone
[0,15)
(15,+x0)
Overall
FLOPs
PointNet++
38.2/48.0
69.7/89.9
65.7/84.6
1.80
AdaFormer
43.2/52.4
72.8/91.4
67.0/86.4
4.52
USRPM(OUrs)
51.9/64.0
74.4/92.1
72.0/89.1
2.42
D.7 ABLATION EXPERIMENTS OF CONDITIONAL GAUSSIAN PERTURBATION
D.7.1 ABLATION THE EXPONENTIAL DECAY RATE IN CONDITIONAL GAUSSIAN PERTURBATION
The exponential decay rate e in CGP controls how rapidly the noise magnitude diminishes relative to the sparsity ratio of the point cloud scene. Excessively high values result in insufficient noise in dense scenes, while overly small values produce excessive noise, which impedes accurate sim- ulation of prediction errors in dense scenes, thereby compromising model's robustness. Therefore, we conduct an ablation experiment to determine the optimal value for the exponential decay rate in CGP. As shown in Table 13
set the exponential decay rate 1.5 in main experiment.
Table 13: Performance using different exponential decay rate in Conditional Gaussian Perturbation.
Success / Precision are reported.
Car
Ped.
Van
Cyc.
Mean
1.3
75.8/86.5
65.5/80.7
64.3/77.8
72.3/93.8
70.2/87.7
1.5
76.7/87.5
67.4/91.9
69.4/82.2
74.9/94.1
72.0/89.1
1.7
77.2/87.5
62.7/89.5
69.6/82.6
74.2/93.9
70.2/88.1
2.0
74.9/85.5
66.1/91.8
64.0/76.1
73.1/93.9
70.1/87.6
D.7.2 EFFECT OF CGP ON SCENES WITH DIFFERENT SPARSITY CHANGING RATE
CGP designed to add noise perturbations training samples based on sparsity levels, simulating inference errors to enhance the model's robustness across different sparse scenes. In Table D.7.2 we have conducted an experiments to validate the effectiveness across different sparse change rate intervals (i.j) KITTI. The results reveal that CGP-trained model still outperforms in scenes with rapid sparsity changes (sparsity rate variation > 0.4), proving that CGP can also function effectively in such scenes, which highlights CGP's stability in scenes with rapidly changing sparsity.
Table 14: Performance comparison of model trained with CGP and without CGP in Suc- cess/Precision across different sparsity changing rate (represented by different intervals [i, j) of changing rates) KITTI.
Method
[0,0,4)
[0.4,1]
Without CGP
70.1/88.2
68. 1/78./
With CGP
72.0/89.2
69.2/80.0
D.7.3 EFFECT OF CGP ON OTHER METHODS
CGP is a universally applicable training data augmentation method that dynamically adjusts the amplitude noise perturbation based on point cloud sparsity to generate diverse training samples. Due to its decoupling from specific algorithmic frameworks, CGP can be seamlessly integrated into any existing tracking method. To validate the generalizability of CGP, we conducted experiments by applying it the siamese-based method BAT et al. 202 and motion-centric method M22Track(Zhen) et al.) (022). As demonstrated in Table (1) CGP significantly increases training sample diversity, particularly benefiting underrepresented category (e.g. BAT achieve +33. 33..1/455.9
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improvement Cyclist.). (2) CGP effectively simulates prediction errors under varying sparsity conditions, thus enhances tracking robustness across all categories. In summary, CGP not only theoretically compatible with other methods but also practically effective in enhancing their tracking performance.
Table 15: Performance comparisons the KITTI dataset. "Improvement" refers the performance gain of our CGP training models over the corresponding counterparts.
Method
Car (6,424)
Pedestrian [6,088]
Van [1.248]
Cyclist (308)
Mean [14,068]
BAT Zheng et al. 2021
60.5/77.7
45.7/72.5
52.4/67.0
33.7/45.4
$1.2/72.8
BAT traine with (Our)
66.3 80.8
45.8/745
43.2/65.0
66.8/91.3
55.8/76.6
Improvement
1.88/1 3.1
1..1/12.
+..8/1-1.8
/3..1/1455 45.9
1.66/
MPTrack Zheng et al. 2022
65.5 80.8
61.5 88.2
53.8 70.7
73.2/
62.9/
M'Track traind with CGP CGP (Ours)
68.3 / 81.2
64.4 89.7
60.3 / 76.3
78.1
66.1 / 84.7
Improvement
128/11.4
1.2
1 5.6
1.9/113
1322/
D.8 EFFICIENCY COMPARISON AND INFERENCE SPEED
As shown in Table 16 we compared PointRePar with other methods in terms of the inference speed, parameter count and computation overhead. Table 17 reports the detailed time consumption. Our PointRePar achieves 37 FPS and the average inference time per frame is 27.3 ms. is noted that, compared methods (e.g. Trrrac(Zhenn et al. 2022) and SiamMo Yang et al. (024 utiliz- ing only two consecutive frames, multi-frame methods (e.g. DeqTrack3D(Lin et al. (2024) and PointRePar) entails more time-consuming Pre-process due the multi-frame input, which explic- itly constrains the overall throughput of our framework.
Table 16: Efficiency comparison on the KITTI dataset.
Method
FPS
Params. (M)
FLOPs(G)
MCTrack
57
2.2
2.48
CXTrack
29
18.3
4.63
MBPTrack
50
7.4
2.88
Seq SeqTrack3D
38
3.7
5.52
PointRePar(OUrs)
37
4.3
6.66
Table 17: Inference time of each component of our model. TSM refers to Temporal Scan Mamba for point level features. LMTR refers to long-term motion trajectory rectification module for box-level features. CA refers to the cross-attention module.
Pre-process 8.8 ms
First-stage
Feature completion
Time
2.1 ms
1.5 ms
USRPM
TSM&LMTR&CA 6,4 ms
Overall
Time
8.5 ms
27.3 ms
D.9 MAMBA VS ATTENTION
We utilize Mamba(Gu & Dao 2023) the core component of our PointRePar for feature encoding, leveraging its strong long-term sequence modeling capabilities and high modeling efficiency. To investigate the effect of Mamba
PointRePar, replace all instances of Mamba with the self- attention operation in PointRePar and compare the performance of this new variant to the original Mamba-based version. The experimental results presented in Table [8] indicate that the Mamba- based version is computationally more efficient than the self-attention variant, while demonstrating slightly better tracking performance. These observations are consistent with the conclusions drawn from the comparison between them in previous studies Liang et al. 2024 Han et al. 2024).
E MORE VISUALIZATIONS
E. CORE MODULES MECHANISM VISUALIZATION
Visualizations of DEA's receptive field: Figure 9 visualizes the adaptive dynamic receptive fields of the DFA module for foreground (Car/Pedestrian) and background points. As shown in Figure 9
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Table 18: Comparison the KITTI dataset between Mamba and self-attention, used as the funda- mental component in our PointRePar, in terms of tracking performance and efficiency. All results reported from evaluations single GeForce RTX 3090 GPU.
Components
Performance
Backbone
Enc&Dec
Pro/Post process
Inference speed
FLOPs
#Params.
Mamba based
72.0/89.1
8.5ms 13ms
6.4ms 8.6ms
12.4ms
36.6FPS 29.4PPS
6.66G
4.3M
Attention based
69.6/87.9
12.4 12.4ms
9.31G
5.6M
00
62
24
66
Foreground Point
Receplive Rold
point
Receptive Felld
Background Point
point
point
Receptive foeld
Receptive field
Car
Pedestrian
Figure 9: Visualization of the receptive field for a single point (green) in the DFA module. When the green point foreground point, its receptive field tends capture point cloud information (red) from other parts of the target to obtain more shape information, while background points tend capture similar background points. This phenomenon demonstrates that the DFA can adapt to shapes of different categories and distinguish foreground from background.
00
04
0.6
0.8
10
Car
Pedestrian
Figure 10: Visualization of Mamba's bidirectional scanning in the USRPM module. The stride
length (cool-warm colormap) represents the activation value each point. Mamba assigns larger strides to foreground points, indicating focused attention after bidirectional scanning, which helps mitigate background interference and enhances target localization accuracy.
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the foreground points tend to capture holistic target features to acquire structural information while avoiding background confusion, whereas background points focus on contextual information with- out mixing with foreground points. This behavior demonstrates how DFA enhances spatial feature discriminability.
Visualization of Mamba bidirectional scanning mechanism: Mamba dynamically regulates the degree of input information flow into the state space through parameter called the step size (A). larger step size value indicates that the current token has more significant impact the hidden state, which can be interpreted as the token playing a more important role in the sequence. There- fore, in Figure [0.. we visualize the step size values processed by Mamba for each point point cloud processing. It shows that Mamba assigns larger strides to foreground points (e.g. the front of car), indicating focused attention after bidirectional scanning, which helps mitigate background interference and enhances target localization accuracy.
E.2 ADDITIONAL QUALITATIVE VISUALIZATIOONS
In Figure II provide the tracking results under more scenes featuring extreme sparsity and rapidly changing sparsity. As shown in the Figure [1 regardless of whether is in extreme sparsity scenarios rapidly changing sparsity scenes, method achieves slightly better tracking results compared to SiamMo.
In Figure 12, provide more visualizations to demonstrate the effectiveness of modeling contin- uous motion for robust tracking with point clouds. As shown Figure [2] PointRePar achieves more accurate orientation estimation in Pedestrian category than SiamMo. For and ob- ject categories, PointRePar demonstrates superior localization precision relative to the SiamMo, excelling particularly under sparse point cloud conditions where conventional methods encounter significant estimation challenges.
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T=70
T=73
T=75
T=77
Car
T=310
T=311
T=312
T=313
Podestrian
T=88
T=90
T-92
T=95
Van
Timelae
SumMo
PoindRaPa(Dunn)
Figure 11: Qualitative comparison between our model and the state-of-the-art category-specific method SiamMo in extreme sparse and sparsity fast changing scenes.
Car
Pedestrian
Van
Ground Truth
SiamMo
PolntRePar(Oum))
Timeline
Figure 12: Qualitative comparison between our model and the state-of-the-art category-specific method SiamMo on challenging samples across categories.
22


Reviews:
[{"review_id": "A6FGGb68FK", "reviewer": "Reviewer_h3CE", "summary": "PointRePar tackles the task of 3D single object tracking, which involves tracking a given 3D bounding box query throughout a sequence of sparse point clouds, ultimately predicting the center and yaw of the bounding box at each frame. Since this task relies solely on point representations, it requires robust shape modeling and effective utilization of temporal features.\nTo address these challenges, the paper introduces a Mamba-based spatial and temporal encoder. Moreover, because the inference process is autoregressive, the model must be resilient to errors propagated from previous predictions. To enhance robustness, the authors propose a dynamic Gaussian perturbation strategy during training, where the perturbation scale is adaptively adjusted according to the local point sparsity.\nFinally, the paper presents a unified model applicable across multiple object categories, demonstrating strong performance on benchmark datasets.", "questions": "I am not very familiar with the field of 3D single object tracking, so my evaluation may not be fully conclusive. The paper is overall well-structured and clearly written. However, I am not entirely convinced that the proposed model is truly novel or provides substantial insights to the community.\n\nThe use of Mamba for point cloud encoding has already been demonstrated in prior work, such as Zhang et al., “Point Cloud Mamba: Point Cloud Learning via State Space Model,” 2024, as well as in several subsequent studies. While applying Mamba to motion trajectory rectification is an interesting direction, its significance seems limited given that Mamba is already well-known for its strong performance in sequential processing.\n\nOverall, my impression is that the introduction of Mamba forms a major part of this paper’s contribution, but the work does not clearly highlight why or how Mamba is particularly effective for this specific task.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Strong performance on the benchmark.\n\n2. An interesting result in the supplementary shows that the model achieves better efficiency and performance when Mamba is used instead of Attention for point cloud processing.", "weaknesses": "1. Although category-unified and category-specific paradigms are listed separately in all tables, I believe a fully unified model could still be trained in the same way as the category-specific one. To ensure a fair comparison, PointRePar should be trained separately for each category and compared directly with the state-of-the-art category-specific models. I am not fully convinced that these paradigms are inherently different to the extent that they cannot be fairly compared in the same table.\n\n2. Although Conditional Gaussian Perturbation is only applied during training, Figure 3 may mislead readers into believing that it is also used during inference.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aYInyRwpgP", "reviewer": "Reviewer_jhyB", "summary": "The paper tackles LiDAR‐based 3D Single Object Tracking (SOT) in a category-unified setting. It proposes PointRePar, a coarse-to-fine tracker that parses spatial and temporal point relations to improve robustness across categories. Experiments on KITTI, nuScenes, and WOD show large gains over prior category-unified trackers and competitive results vs category-specified SOTA.", "questions": "1. Will CGP works for other methods?\n\n2. Could you clarify how each ablation in Table 3 is instantiated? For example, in the ‘w/o USRPM’ setting, is USRPM simply removed, or replaced with a capacity-matched alternative (e.g., a PointMamba-style block)?”\n\n3. How big is the gap between the coarse-stage predictions and the refined predictions?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Clear motivation for unified tracking and robust spatio-temporal relation parsing; the goal and assumptions are explicitly stated\n\n2. Well-structured architecture: a coherent coarse-to-fine pipeline with motion-based coarse prediction, spatial relation parsing (USRPM+DFA), temporal relation parsing (TSM+LMTR), and a cross-attention decoder.\n\n3. SoTA performance.", "weaknesses": "1. Limited novelty, the paper’s novelty is mainly compositional/system-level, not a fundamentally new primitive.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4wzK1Rqfsl", "reviewer": "Reviewer_ncKM", "summary": "This paper presents PointRePar, a category-unified 3D single object tracking (SOT) method that leverages spatiotemporal point relation parsing to achieve robust tracking across multiple object categories. The authors propose a Mamba-based U-Net architecture for multi-scale spatial relation modeling, a dynamic feature aggregation (DFA) mechanism, a long-term temporal relation parsing module, and a conditional Gaussian perturbation (CGP) scheme to enhance robustness. Extensive experiments on KITTI, NuScenes, and Waymo Open Dataset demonstrate that PointRePar outperforms existing category-unified methods and is competitive with state-of-the-art category-specific trackers.", "questions": "See weakness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper introduces a unified tracking framework that effectively combines spatial and temporal relation parsing using Mamba-based architectures, which is innovative.\n\n2. The paper focuses on the unified category 3D SOT tracking, which has solid contributions. Most 3D trackers are category-dependent, which is unnecessary and introduces significant redundancy into 3D SOT trackers.\n\n3. The results are comprehensive and good.", "weaknesses": "1. The overall method is too complex, which includes a U-Net for feature fusion, motion-modeling, and dynamic feature aggregation.  \n\n2. The experimental results should clearly reveal the effects of each module and the hyperparameters design for the proposed module. \n\n3. The speed comparison and ablation studies should be clearly presented.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nwe9kpXKc9", "reviewer": "Reviewer_pyGc", "summary": "This paper proposes the Point Relational Parsing (PointRePar) model, supporting multi-category joint training. Robust tracking is achieved through two core designs: First, the U-shaped Spatial Relational Parsing Module (USRPM) based on Mamba, combined with Dynamic Feature Aggregation (DFA), captures multi-scale spatial point relationships to enhance foreground - background feature separation. Second, it employs dual-level temporal relationship modeling at both point-level (Temporal Scan Mamba) and bounding-box-level (Long-term Motion Trajectory Rectification, LMTR), while introducing Conditional Gaussian Perturbation (CGP) to simulate prediction errors related to scene sparsity. Experiments validated on KITTI, NuScenes, and WOD benchmark datasets demonstrate that PointRePar not only significantly outperforms existing category-unified methods but also rivals current state-of-the-art category-specific approaches. This provides a balanced solution for 3D SOT that optimizes “generalizability, performance, and training efficiency”.", "questions": "1. Regarding Mamba's advantages over USRPM: Compared to Transformers or RNNs, is there quantitative evidence of Mamba's “long sequence modeling advantage” on unstructured point cloud data? For example, under identical FLOPs, how does Mamba's feature separation (e.g., foreground-background cosine distance) compare to Transformers?\n\n2. Regarding CGP's Generalizability: CGP is designed based on the “scene sparsity - error magnitude” correlation. If scene sparsity changes drastically over time (e.g., when an object is momentarily occluded by a building), can CGP's dynamic noise adjustment still function stably? Can qualitative tracking results for such dynamic scenes be provided?\n\n3. To enhance the literature context and highlight research relevance, it is suggested to add references to related work:\n[1] Instance-guided point cloud single object tracking with inception transformer, IEEE TIM 2023;\n[2] Revisiting Siamese-Based 3D Single Object Tracking With a Versatile Transformer, IEEE TPAMI 2025.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1. Existing methods predominantly employ “category-specific training,” requiring separate models for each category, resulting in poor generalization and low training efficiency. Conversely, few unified category methods (such as CUTrack) suffer from insufficient feature discriminative power and weak motion modeling, leading to performance significantly lagging behind category-specific approaches. PointRePar achieves “unified category training + category-specific SOTA performance” for the first time, resolving the core challenge of balancing “cross-category generalization” with “high-performance tracking.”\n\n2. Spatial modeling breaks traditional frameworks: Combining Mamba (high-efficiency long sequence modeling) with a U-shaped structure to design USRPM, paired with DFA for dynamically adjusting point feature receptive fields — — simultaneously resolving the low feature separability issue in PointNet++ and AdaFormer (validated by Figure 2 t-SNE plot) while outperforming Transformer-based methods in efficiency (36.6 FPS inference speed). This achieves a balance between “multi-scale spatial relationship analysis + low computational overhead.”\n\n3. Temporal Modeling Addresses Fine-Grained & Long-Range Requirements: Point-level modeling captures pixel-level motion features between frames, while bounding-box-level LMTR refines trajectories using historical bounding-box sequences. This dual-level design balances “fine-grained motion details” with “long-range trajectory consistency”; CGP dynamically adjusts noise based on scene sparsity, better matching real-world error patterns than traditional uniform noise (Figure 4 validates sparse scene error patterns), significantly enhancing robustness.\n\n4. Strong Module Synergy: The workflow design—“coarse prediction (lightweight tracker) → spatial feature encoding (USRPM) → temporal relationship analysis (TSM+LMTR) → fine decoding (cross-attention)”—forms a complete “spatio-temporal fusion → error robustness → precise localization” chain, with complementary yet non-redundant module functions.\n\n5. Comprehensive Benchmarking: Performance surpasses category-unified methods (MoCUT, TrackAny3D) by over 15%, while matching category-specific SOTA (SiamMo, MBPTrack), challenging the assumption that category-unified approaches inherently underperform category-specific ones.\n\n6. Thorough ablation studies: Validated the effectiveness of four core modules—DFA, USRPM, LMTR, and CGP (Table 3)—analyzed the impact of hyperparameters (DFA offset points, LMTR sequence length) on performance (Tables 4 and 5), and even supplemented efficiency comparisons between Mamba and self-attention (Table 13), yielding highly credible results.", "weaknesses": "1. Insufficient analysis of extreme scenarios: While the paper mentions strong performance in sparse scenarios, it lacks dedicated analysis for extreme cases such as “heavy occlusion (target point cloud < 10 points)” or “rapid viewpoint changes (e.g., sharp vehicle turns).” It also fails to compare behavioral differences with SiamMo in such scenarios, making it difficult to fully support the conclusion that “robustness surpasses category-specific methods.”\n\n2. Lack of core module mechanism visualization: Designs like USRPM's Mamba bidirectional scanning and DFA's dynamic receptive field adjustment are validated solely through performance improvements. Without supplementary feature visualizations (e.g., attention weight maps for different-scale features) or error heatmaps, it remains difficult to intuitively demonstrate “how spatio-temporal relationship analysis enhances tracking accuracy.”", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)