Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection
Authors: Huang-Yu Chen
Jia-Fong Yeh
Jiawei
Pin-Hsuan Peng
Winston H. Hsu
Venue: CoRL 2024 (2024)
Keywords: Active Learning
LiDAR 3D Object Detection
Autonomous Driving
TL;DR: DDFH leverages a dual-component approach for LiDAR-based 3D object detection, combining Instance-level Distribution Discrepancy with Frame-level Feature Heterogeneity to enhance data informativeness and reduce annotation costs effectively.

Abstract: LiDAR-based 3D object detection is a critical technology for the development of autonomous driving and robotics. However, the high cost of data annotation limits its advancement. We propose a novel and effective active learning (AL) method called Distribution Discrepancy and Feature Heterogeneity (DDFH), which simultaneously considers geometric features and model embeddings, assessing information from both the instance-level and frame-level perspectives. Distribution Discrepancy evaluates the difference and novelty of instances within the unlabeled and labeled distributions, enabling the model to learn efficiently with limited data. Feature Heterogeneity ensures the heterogeneity of intra-frame instance features, maintaining feature diversity while avoiding redundant or similar instances, thus minimizing annotation costs. Finally, multiple indicators are efficiently aggregated using Quantile Transform, providing a unified measure of informativeness. Extensive experiments demonstrate that DDFH outperforms the current state-of-the-art (SOTA) methods on the KITTI and Waymo datasets, effectively reducing the bounding box annotation cost by 56.3% and showing robustness when working with both one-stage and two-stage models.

Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection
Huang-Wi Yu Chen' Jia-Fong Yeh!
Jia-Wei Liao'
Pin-Hsuan Peng' Winston H. Hsu
National Taiwan University
MobileDriiv
Abstract: LiDAR-based 3D object detection is a critical technology for the devel- opment of autonomous driving and robotics. However, the high cost of data an- notation limits its advancement. We propose a novel and effective active learning (AL) method called Distribution Discrepancy and Feature Heterogeneity (DDFH). which simultaneously considers geometric features and model emheddings, sessing information from both the instance-level and frame-level perspectives. Distribution Discrepancy evaluates the difference and novelty of instances within the unlabeled and labeled distributions, enabling the model to learn efficiently with limited data. Feature Heterogeneity ensures the heterogeneity of intra-frame instance features, maintaining feature diversity while avoiding redundant or sim- ilar instances, thus minimizing annotation costs. Finally, multiple indicators are efficiently aggregated using Quantile Transform, providing unified measure of informativeness. Extensive experiments demonstrate that DDFH outperforms the current state-of-the-art (SOTA) methods on the KITTI and Waymo datasets, ef- fectively reducing the bounding box annotation cost by 56.3% and showing ro- bustness when working with both one-stage and two-stage models. Source code: -aDtiea-----eeddeer
Keywords: Active Learning, LiDAR 3D Object Detection, Autonomous Driving
Instance-Level Distribution Discrepancy
Uniabsled
Geometric
Labeled
different
Quantile Transform
Detector
Embedding
Frame-Level Feature Heterogeneity
X
X
infer
std 0.8,
0.9, com
Informativeness
(a) Multiple features
(b) Multi-level information
(c) Scores alignment
Figure 1: The three core concepts of DDFH. (a) Embedding and geometric features used as DDFH inputs. (b) Considering instance-level distribution discrepancy and frame-level feature het- erogeneity ensures that instances remain highly informative across all levels. (c) After transforming various indicators using the Quantile Transform. effectively aggregates to estimate the final infor- mativeness.
8th Conference Robot Learning (CoRL. 2024), Munich, Germany.

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Introduction
Research on LiDAR-based 3D object detection [1, 2] has emerged due to its significant potential and applications. However, annotating LiDAR data requires locating multiple objects in three- dimensional space, which is expensive and time-consuming. Therefore, obtaining annotated data more efficiently within limited time and resources has become crucial and unavoidable issue. Several studies have attempted reduce annotation costs through Auto Labeling [3, 4] or Domain Adaptation [5, 6]. which rely small number of annotated samples. However, unstable annotation accuracy and restricted domain shifts may limit their usage in various applications.
Active Learning (AL), aiming to select the most informative samples from a large pool of unla- beled data for human annotation, is a significant solution to reduce the model's dependence on the amount of data. Although AL has been proven effective in mitigating annotation costs in various research domains [7. 8], its application to LiDAR-based 3D Object Detection is underexplored, with three main challenges remaining unresolved: (1) Compared to 2D object detection, LiDAR- based object detection has additional geometric features (such rotation and point density) that need to be considered. (2) While the detector's predictions are instance-level the selection in AL is frame- level, making it challenging to propagate from instances to frames and estimate informativeness. (3) Aggregating multiple indicators under different units and scales is also challenging.
Previous work has used general AL strategies such as entropy and ensemble methods to estimate un- certainty. However, they neglected the unique geometric features in LiDAR -ased object detection (first challenge), and solely assessing the instance-level informativeness is insufficient to address the second challenge. A recent work, CRB [9], proposed three heuristic methods to estimate label balance, representativennsss, and point density balance through a staged filtering approach. Unfortu- nately, the filtering order significantly affects sampling results, impacting the fairness among indica- tors and failing to solve the third challenge. Another work, KECOR [10], proposed kernel coding rate maximization strategy. However, it also did not consider geometric features and used different weighted settings to aggregate multiple indicators in different datasets, affecting generalization.
To address the above challenges, we propose the Distribution Discrepancy and Feature Heterogene- ity (DDFH) method, as illustrated in Fig. where components (a), (b), and (c) are designed for the first, second, and third challenges, respectively. DDFH leverages model embedding and geomet- ric information as features for informativeness estimation to address the first challenge. Moreover, explores informativeness from instance-level and frame-level perspectives by considering intra- class Distribution Discrepancies (DD) and intra-frame Feature Heterogeneity (FH) to tackle the second challenge. Then, DDFH employs Quantile Transform (QT) to normalize each indicator to the same scale, effectively aggregating the indicators to solve the third challenge. Finally, we propose Confidence Balance (CB) to evaluate the allocation of annotation resources. Unlike previ- ous methods that solely count selected instances for each category, CB considers the summation of confidence levels for each instance within the same category.
We verify the effectiveness of DDFH through experiments on real-world datasets, KITTI and Waymo Open Dataset. The results indicate that our DDFH method outperforms the existing SOTA, effectively reducing the data annotation cost by 56.3% and achieving an average improvement of 1.8% in 3D mAP with the same amount of data. From our extensive ablation studies, DDFH also demonstrates its generalization when used with both one-stage and two-stage detection models.
Related Work
LiDAR-based 3D Object Detection. LiDAR-bases object detection techniques are primarily di- vided into two categories: point cloud direct processing and voxelization. Methods such as the PointNet series [11, 12] operate directly on point clouds, preserving the original spatial accuracy of the data but are less efficient handling large-scale data. Recent research, like PointAugment- ing [13], introduces cross- cross-modal augmentation, enhancing LiDAR point clouds with deep features extracted from pre-trained 2D object detection models, thereby improving 3D object detection per-

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formance. Voxelization methods, such as VoxNet [14] and SECOND [15], convert point clouds into voxel grids to enable efficient 3D convolution, significantly increasing computational speed. The Voxel Transformer (VoTr) [16] architecture effectively expands the model's s receptive field, en- hancing the ability to capture large-scale environmental information. The PV-RCNN series [17, 18] improves detection accuracy and processing efficiency by fusing point cloud and voxel features. These detectors rely heavily large volumes of high-quality training data; however, the labeling cost for LiDAR data is quite expensive.
Active learning for object detection. Active learning selects the most informative samples for an- notation, thus mitigating the model's dependence on the volume of data. Numerous general active learning strategies currently exist, such as those based on model uncertainty [4, 19, 20, 21, 22]. diver- sity [23, 24, 25], or hybrid methods that combine both approaches [26]. Estimating in the gradient space is also common approach (e.g., BADGE [27], BAIT [28]). Research applying these methods to object detection [29, 10, 31, 32] remains limited. Many studies directly use general strategies like maximum entropy [33], bayesian inference [34] to estimate uncertainty in both bounding box and category. LTVC [35] introduces noise-perturbel samples and assesses tightness and stability based on the model's output. The estimation of information quantity through the output probability dis- tribution also common approach [36, 37]. Research on LiDAR -bbaed object detection even scarcer, mainly due the high computational cost of point cloud processing and the higher dimen- sionality of regression information. General AL methods (Shannon Entropy [38], ensemble [29]) do not consider geometric features and therefore are not well-suited for LiDAR. Recent work, such as CRB [9], proposes three heuristic methods to incrementally filter samples. KECOR [10] identifies the most informative samples through the lens of information theory. However, these studies fail to effectively integrate multi-level information and do not consider the distribution of selected samples, leading to redundant annotation costs. Therefore, we propose DDFH, which estimates the informa- tiveness based on the distributional differences of instances and intra-frame feature heterogeneity, and effectively aggregates multiple indicators using the quantile transform estimate multi-level informativeness.
3 Methodology
3.1 Active Learning Setup
In the active object detection setup, the labeled set D = ((P,yY4) contains small amount of point clouds p4 with annotations Y and Dú {Pt} } represents a large unlabeled of raw point clouds Pu. Initially, samples are randomly selected to form DL, and the detection model learns over multiple rounds E {1,..., Rt). The objective of active learning is to evaluate the informativeness of Py in each round and select the most informative samples to form a new subset D for human annotation. Then, the D is merged into D, to start a new round to retrain the model. This process repeats until the size of the labeled set reaches the annotation budget.
3.2 Framework Overview
We propose a novel active learning framework, Distribution Discrepancy and Feature Heterogeneity (DDFH) for LiDAR-based 3D object detection. As illustrated in Fig. 2, infer point clouds P E (Dv, Dr. into the model and get model output containing embeddings and bounding boxes. However, estimating distributions in high-dimensional space is challenging, so we use t-SNE[40] to project embeddings into lower dimensions while retaining important information, denoted as fm E R2. The geometric features of LiDAR- based object detection (length, width, height, volume, rotation, and point cloud density) f9 E R are also significant, as they convey direct information about objects such as occlusion, behavior, and morphology. So we use f = | fz TJT R as the input feature for DDFH, calculating multiple indicators estimate informativeness. However, since the units of the indicators differ, normalization is required before aggregation.
3

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Instance-Levee Distribution Discrepancy
nilll
Plint Deaily
Class
S nuo
labeled features
Gaussian Micture Modsl
probabiity density
Sue Siy
Infer
Boxe
Sa
Quartle
box regression
Cless
unlabeled features
Gaussian Misture Model
probubility density
Detector
HSKE
Frame-Level Feature Heterogeneity
embedeing
ya
Iad
ineer
S cor
compute p
1
update
Haman Ansotation
S Spor
weesge
Do
Single Frame
Select top
Figure 2: DDFH framework for LiDAR biDeR-based 3D active object detection. According to the batch active learning[39] setup. one cycle represents a single sampling. DDFH utilizes Quantile Trans- form to normalize all metrics before aggregation to estimate informativenes, and then updates the dataset before starting new round.
Score Normalization. DDFH evaluates informativeness based on multiple indicators, but their scales differ, making direct aggregation impossible. Thus, we use Quantile Transform &b to nor- malize. v'C., .) is a non-linear transform that converts the first input to follow a normal distribution and returns the transformed result of the second input. spreads out the most frequent values and reduces the impact of outliers. Since the goal of active learning is select the top samples, the relative distance of scores is not particularly important. Instead, maintaining the ranks of various indicators and reducing outliers aids in aggregating the indicators, making / crucial bridge in computing Iotal. Next, we will introduce the operating principles of DDFH sequentially.
3.3 Instance-Level Distribution Discrepancy
Since labeled much smaller than the unlabeled set, estimating the distribution particularly important. Reducing the distribution gap between the labeled set and the unlabeled set will assist the model in inference. Inspired by [37]. we use a Gaussian Mixture Model(GMM) to estimate proba- bility density. Unlike previous works, we consider both geometric features and embeddings, using dimensionality reduction to avoid overly sparse space. Intuitively, if an instance appears frequently in the unlabeled set but is rare in the labeled set, such an instance can help the model efficiently un- derstand unlabeled samples. Therefore, for each class C, we establish G fit on (fe.1...fe.Ng) and G fit on (fe.1..ff.,n)) J. where Gh is GMM fit on unlabeled features, and N is the number of instances with class e in the unlabeled set. We estimate the probability density function of cach instance in the unlabeled and labeled sets, and calculate the discrepancy score s!dd
dd Pay - (fe.s) = 1, N. c.
(1)
where Poy of.,)) represents the probability density of f. in the unlabeled set. However, considering Saa alone is insufficient, very dense instances might overly influence the indicator, leading to frequent selection dense but repetitive instances in the early stages. Hence, unlike previous works [7], we also extract Pag (f),) to calculate the novelty score s noo;
Se,5 -P (fo.). i= 1,2 NV.
nov ensures the novelty of instances. an instance has a high probability density in the labeled set, it indicates that the instance has already been selected, thus the s nov score will decrease, effectively reducing redundant annotation costs. Following these two indicators, (P;) can be calculated as:
Iaa(P;) N
Srdd s. + (S ,6,00 nov
(3)

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where N is the number of instances in P,. Sdld is the set of all instances' s Ablation studies show that aa enables the active learning model to learn rapidly with a small amount of data. However, dd does not consider frame-level information, which may overlook similar instances within the same frame. Therefore, we introduce second component to address this issue.
3.4 Frame-Level Feature Heterogeneity
Object detection is a multiple-instance problem. Considering only instance-level information can lead to redundant annotations. Complex scenes usually contain numerous objects that share the same lighting and environmental factors, making their features highly similar or following linear variations. Such samples are costly to annotate but offer limited assistance to the model. There- fore, we propose frame-level Feature Heterogeneity (FH). As shown in Fig. Ib, we decompose heterogeneity into correlation and variance. Denote f,c "f..c.,...ff...... all instances with class in j-frame, where represents the number of instances. FH ensures Fjęe can maximize the feature heterogeneity of the labeled instance vector F.e. Specifically, we combine the two into F,cc [F F.1 E R where N as the number of all sample. We use covariance coo and variance o² to calculate the Pearson correlation p. ensuring non-linear variations among features. The correlation p is calculated as:
the feature vector of
2 N
EK E j. j.c Fjc))
cou(
p(F,,c) Ny 1) keF a (F o aFF
where Ft. and Fe J. represent the k-th and E-th feature dimension of matrix Fj.c, and j.c. is the mean of F& (F),)) N (1 J.c The smaller the value of p. the less linear the correlation, enabling the model learn more feature combinations. However, as shown in Fig. I, considering only correlation overlooks the information about the distance between features. Thus, we also consider o² to ensure sufficient variation among features. Based on these two indicators, we calculate s sear and s war;
Fk J.c.ll 112 Fk N, is the number of feature dimensions of matrix Fj,--
N
PAL J.C
(F J.c
con j.c.
= =1 1p111p(Fj,))
(5)
The closer P is to O, the less linear the correlation between features. Conversely, values far from 0 indicate positive or negative correlations. Therefore, we take the absolute value correlation and subtract it from 1, making the 5, indicator larger. Based on these two scores, we calculate the feature heterogeneity informativeness ln (P₁) for Py:
C (Sear::57 up(Soor; $
[n(()) = : Σ C
(6)
c=1
where Svar is the set of all gdd, Lyn ensures instances maintain low correlation and high variance, calculated through multiplication. This introduces the core components of the DDFH approach, exploring informativeness from both instance-level and frame-level perspectives.
3.5 Confidence Balance for Imbalanced Data
Balancing annotation costs across classes has always been crucial in active learning. Previous works [10, 9] calculate entropy by the number of all categories and the classification logit from the clas- sifier, termed Label Balance (LB). However, these method is limited in imbalanced datasets, as imbalanced classes usually have lower confidence, resulting in more false-positive instances. The actual quantity is often less than expected. Therefore, we propose Confidence Balance (CB) Tas, replacing instance quantities with the sum of confidences in each category to better reflect the true class distribution in the frame, enhancing the number of minority classes. Iss can be calculated as follows:
Lot Lob(P)) - Σ ((pc)) ) log o(ppc); d(pc))
CPii
c esi.
5

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Table 1: Compare 3D m)) scores for general AL and AL for detection in KITTI Dataset with two-stage 3D detector PV-RCNN
AVERAGE
CAR
PEDESTRIAN
CYCLIST
Method
Fasy
Mod.
Hard
Fasy
Mod.
Hard
Fasy
Mod.
Hard
Easy
Mod.
Hard
Generte
CORESET [24)
72.26
59.81
55.59
87.77
77.73
72.95
47.27
41.97
38.19
81.73
59.72
55.64
BADGE [41]
75.34
61.44
56.55
89 96
75.78
70.54
51.
40.98
45.97
84.11
62.29
58.12
LLAL (42)
73.94
62.95
58.88
89.95
78.65
75.32
46.94
45.97
45.97
75.55
60.35
55.36
LT%c [35]
75.88
63.23
58.89
88.
78.12
74.87
$5.17
48.37
43.63
83.72
63.21
59.16
Mc-REG [9]
66.21
51.70
88,
73.87
35.82
29.79
73.98
51.85
Delection
Mc-MI [38]
71.19
57.77
53.81
86.28
75.58
71.56
41.05
37.50
33.83
86.26
60.22
56.04
CONSENSUS [29]
75.01
61.09
57.60 70.
90.14
78.01
74.28
56.43
49,50
44,80
78.46
55.77
53.73
[9]
79.06
90.81
79,06
CRB(off.)
80.70
67.81
62.81
90.98
79.02
74.04
64.17
50.82
50.82
86.96
67.45
63.56
KECOR [10]
79.81
67.83
62.52
91
79.63
74.41
63.49
56.31
50,20
84.51
67.54
62.96
(ECOR(Offi.
81.63
68.67
63.42
91.71
74.05
65.37
57.33
51,56
87.80
69.13
64.65
(DFH(OUus)
82.27
69.84
64.76
91.76
80.65
76.46
66.37
59.40
52.97
88.68
69.47
64.85
W0.000
Lid
Res
(2309374))
Number of Selected Bomes
.urbbe of Selocted Bowes
Number of Selecceedd Boees
Figure 3: 3D mAP(%) DDFH and AL baselines on the KITTI val split with PV-RCNN.
where Pi. represents the sum of confidences of all instances of class e the j-th frame. We compare the effectiveness of LB and CB sampling in Fig. 4(c-d), demonstrating the importance of Ieb for imbalanced data.
3.6 Acquisition Function
As described in 3.2, DDFH combines multiple indicators to explore informativeness comprehen- sively. Through QT, all indicators are normalized to compute unified informativeness indicator Looal, ensuring that the top-k frames are efficient, novel, heterogeneous, and balanced, identifying the optimal selected sets D", formulated as:
D = arg max Trotal (Pt); Trotat (P;) D,CDv
(P,)))) (Ta))))))TT(())))).
Iaa and In evaluate informativeness, and multiplying by Leb ensures balanced annotation costs across classes while considering informativeness.
4 Experiments
4.1 Experimental Settings
3D Point Cloud Datasets. We tested our method on two real-world datasets: KITTI [43] and Waymo Open Dataset [44]. KITTI contains approximately 7,481 training point clouds (3712 for training. 3769 for validation) with annotations. Each point cloud annotated with 3D bounding boxes for cars, pedestrians, and cyelists, totaling 80,256 objects. The Waymo Open Dataset provides large-scale collection of data, it contains 158,361 training point clouds and 40,077 testing point clouds. The sampling intervals for KITTI and Waymo are set to I and 10, respectively.
Baselines. We comprehensively evaluated 6 general active learning (AL) methods and 6 AL meth- ods for object detection. RAND selects samples randomly. ENTROPY [45] and LLAAL [42] are uncertainty-based methods. CORESET [24] is a diversity-based method. BAIT [28] and BADGE [41] are hybrid methods. MC-MI [38] and MC-REG [9] utilize Bayesian inference. CONSENSUS [29] employs an ensemble to calculate the consensus score. LTTC evaluates instability and local-

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120
0.8
12.
contact
000
1001 an
Numberd" Selected Doom
Number of Selected Soxss
number of Selected Doons
Mumber of Activs Round
(a)
(b)
(c)
(d)
Figure 4: (a-b) report 3D APH of various AL. methods on different difficulties in the Waymo dataset. (c-d) are experiments
the KITTI dataset. (c) demonstrates the impact on performance when
DDFH omits geometric features and replaces confidence balance with label balance. (d) calculates the entropy of the number of samples selected for each class compare the effectiveness of different AL methods in balancing annotation costs.
ization tightness. CRB [9] progressively filters based on three heuristic methods. KECOR [10] identifies the most informative sample through the lens of information theory.
Evaluation Metries. We follow the work of KECOR [10]: In the KITTI dataset, we utilize Average Precision (AP) evaluate object location in Bird Eye View and 3D, calculated with 40 recall posi- tions. The task difficulty is categorized as EASY. MODERATE (MOD.), and HARD based on the visibility, size, and occlusion/ truncation of the objects. In Waymo, we use Average Precision with Heading (APH), which incorporates both bounding box overlap and orientation accuracy. It cate- gorizes objects into Level 1 (at least five LiDAR points, easier to detect) and Level (all objects, including more challenging scenarios).
Implementation Details. We strive to avoid excessive parameter tuning by using unified set of hyper-parameters across all experiments. We set the perplexity for t-SNE to 100. For Gaussian Mixture Model, set number of components to 10, covar to le-2 to increase generalization and initialize parameters by k-means++.
4.2
Main Results
Since the most baseline method do not provide initial selection samples, to present the results more fairly, reproduce most of the baseline methods using the same initial settings and provided the official reported performance of two recent methods (CRB and KECOR) in experiments.
DDFH with Two-Stage Detection Model. For the KITTI dataset, We ran each method three times. As show in Fig. 3, our method outperforms all baseline methods. Compared with the CRB and KECOR, the annotation cost reduced by 51.7% and 33.2%, respectively, especially when the num- ber of annotations is small, the growth rate particularly fast. In Table we follow the KECOR setting, showing the performance with 800 (1%) bounding box annotations. Under the same initial- ization conditions, our average performance surpasses the SOAT by 2.23%, especially improving by 4.17% in the imbalanced class (Cyclist). For the Waymo Dataset, in Fig. 4(a-b), the Level-1 and Level-2 performance are reported, respectively. Compared to KECOR and CRB, DDFH im- proves APH by 1.8% and 3.8%, respectively, and reduces the anmotation cost by 56.3% and 66.4%, respectively, demonstrating the effectiveness of DDFH in more diverse and complex scenarios.
DDFH with One-Stage Detection Model. Table reports the 3D and BEV mAP scores with 1% annotation bounding boxes. Compared to the SOTA, it improves by approximately 2.8% in 3D mAP and 2.28% in BEV mAP. In Fig. 5, we further report the performance growth trend for each category in different levels of difficulty in MOD. DDFH, especially in the car category, which often leads to excessive annotations, can quickly improve performance with the most streamlined annotation cost. For the pedestrian category, the uncertainty- -bsee method performs exceptionally well with a small number of annotations. However, the lack of consideration for diversity limits the performance.
Ablation Study

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Table Compare 3D mAP and BEV scores for general AL and AL for detection in KITTI Dataset with one-stage 3D detector SECOND
3D Detection mAP
BEV Detection mAP
Method
Venue
EASY
MOD.
HARD
EASY
MOD.
HARD
RAND
66.67
53.15
49.12
72.65
60.94
57.12
ENTROPY [45)
IJCHN'14
69.29
56.58
51.59
74.52
63.38
58.65
BALD [2]]
ICML'17
68.78
55.49
50.30
74.21
62.51
57.70
CORESET
ICLR'18
65.96
51.79
47.65
73.85
60.22
56.06
LLAL (42)
CVPR 119
68.51
56.05
50.97
74.57
63.64
58.92
BADGE (41)
ICLR'20
69.09
55,20
50.72
75.12
63.05
58.81
BAIT [28]
NeurIPS
69.45
55.61
51.25
76,04
63.49
53,40
CRB [9]
ICLR 23
71.69
57.16
52,35
78.01
64.71
60.04
KECOR (10)
ICCV
71.85
57.75
52.56
78.30
60.15
(DFH(Ouus)
74.13
60.61
$5.48
79.68
67.95
63.10
30 mdPTN) Wedoratt
10000 Mal-30 DE
BAN0 CONCECT
KAND CORESET
BIAD
CORESET
LUAL
LLAL
ULA
BADGE
EADGE
BHOGS
CRD
CRD
CR
KECD1
500
Number
.003
1300
2013
2500
3000
200
4000
560
500
1000
Number Selected
100
150
230
250
Figure 5: 3D mAP(%) of DDFH and the AL Baseline across various categories the KITTI dataset at the moderate difficulty with SECOND.
Efficacy of Confidence Balance. As shown in Table 3, CB effectively improves the 3D mAP of the imbalanced class (Cy- clist) by 4.3% compared to LB. From Fig. 4c, it is evident that the performance using LB decreases by average of 2.8% 3D mAP in each round. In Fig. 4d, we further demonstrate the label entropy of the se- lected samples. DDFH can effectively al- locate annotation resources in each round,
Table 3: Compare the impact of each component 3D mAP scores KITTI dataset at the moderate difficulty
DD
FH
CB
LB
Average
Car
Pedes.
Cyclist
62.97
79.44
48.93
60.52
v
66.87
78.89
$6.36
65.38
v
67.69
78.41
54.96
69.71
/
64.73
80.15
51.09
62.94
68.94
79.94
58.22
68.66
69.84
80.65
59.40
69.47
while KECOR sacrifices balance while considering informativeness.
Efficacy of Distribution Discrepancy and Feature Heterogeneity. The results in Table 3 show that DD can significantly enhance overall performance, especially in categories with fewer instances, such as pedestrians and cyclists, by focusing more on the selection of these categories due to their larger distribution differences. Using FH alone to estimate informativeness without considering the labeled distribution would be too limiting. The experiments demonstrate that DDFH effectively combines the advantages of both components, resulting in improvements across all categories.
Efficacy of Geometric Features. In Fig. 4c, the results show that after considering geometric features, DDFH improves the average performance by 1.6% in 3D mAP, confirming that diverse geometric features help the model capture a wider variety of objects.
Conclusion
We propose a novel active learning framework DDFH for LiDAR-based 3D object detection that integrates model features with geometric characteristics. By exploring point cloud data through instance-level distribution discrepancy and frame-level feature heterogeneity, and introducing con- fidence balance, enhance annotations for imbalanced classes. Our extensive experiments show that compared to SOTA, DDHF reduces annotation costs by 56%, improves performance by 1.8%, and efficiently extracts richer information, demonstrating its effectiveness over current methods.

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Acknowledgments
This work was supported in part by National Science and Technology Council, Taiwan, under Grant NSTC 1 1222263--F0022-066 We are grateful to MobileDrive and the National Center for High- performance Computing.
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with flad Supplementary Material of Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection
Table Compare 3D mAP(%) scores for different SOTA apporch in KITTI Dataset when acquiring approximately 1% queried bounding boxes. indicates the reported performance of the backbone trained with the 100% labeled set.
AVERAGE
CAR
PEDESTRIAN
CYCLIST
Method
Easy
Mod.
Hard
Easy
Mod.
Hard
Easy
Mod.
Hard
Easy
Mod.
Hard
CRB
79.06
66.49
61.76
90.81
79.06
74.73
62.09
$4,56
48.89
84.28
65.85
61.66
CRB(offi)
80.70
67.81
62.81
90.98
79.02
74.04
64.17
50.82
50.82
86.96
67.45
63.56
KECOR [2]
79.81
67.83
62.52
91.43
79.63
74.41
63.49
56.31
50,20
84.51
67.54
62.96
KECOR(ofii.)
81.63
68.67
63.42
91.71
79.56
74.05
65.37
57.33
51.56
87.80
69.13
64.65
DDFFH(Ous)
82.27
69.84
64.76
91.76
80.65
76.46
66.37
59,40
52.97
88.68
69.47
64.85
PV-RCNN
81.75
70.99
67.06
92.56
84.36
82.48
64.26
56.67
51.91
88.88
71.95
66.78
10.0
70.0
Modorote
65
mAb//9 -Moderaate
15.0
65.0
mARPS)
$0.0
80.0
GMN component
50
50.3
B4DGE
COCOR
SOINOoni
35.0
component
55.0
45
52.
200
400
600
920
1002 1200 1450 1800
53.5
200
400
2000 1202 1820 1800 Number of Selected Boores
660
Number Active Round
Number Selected Bores
(a)
(b)
(c)
Figure 6: (a) report 3D mAP of various AL methods on KITTI in each active round. (b-c) represents the impact of different density estimation methods and varying parameter settings on the perfor- mance of DDFH.
A More Implementation Details
To ensure the fairness and reproducibility of our experiments, we implemented DDFH and re- 3 produced most of the baselines based on the public ACTIVE-3D-DET toolbox. We followed all KECOR training settings, using Adam as the optimizer, and a onecycle learning scheduler with an initial learning rate of 0.01. The batch size was 6, and each active round was trained for 40 epochs before proceeding
sampling round. We used KITTI and Waymo is approximately 5 and 81 GPU hours, respectively. The model embeddings f used in our method are extracted from the second convolutional layer in the shared block of PV-RCNN.
NVIDIA RTX A6000 complete
all experiments. The runtime for an experiment
B More Experimental Details
11 DDFH in the KITTI Dataset. In Fig. 6a, we present the performance of various AL methods
12 in each active round. The number of point clouds each active round fixed, allowing us to
13 compare the performance models under conditions where they have seen the same number of
14 scenes. Notably, KECOR's performance is below expectations given the same number of frames,
15 indicating that KECOR does effectively consider the diversity information of the scenes. In contrast, DDFH considers frame-level information to avoid redundant instances in similar scenes.
The results show that DDFH has a significant advantage in each active round. We present more

---

43 the impact of uneven distribution different levels, decrease redundant annotations, and effec- 3D mASS/ Eany
BARD
RAND
MC-85
MICR00
CORRSET
CORRSPT
COREART
BALD
SALD
BROGE
B4DGE
BROGE
DOFROOURS
Number of Selected Boxes (Car)
Number of Salected Boxes (Pedestrian)
Kumber of Selected Boxes (Cyclltt
Figure 7: 3D mAP(%) of DDFH and the AL Baseline across various categories on the KITTI dataset the moderate difficulty with PV-RCNN.
30 m4nrg))
ENTROOPY
ESmoree COR8547
ESTOOrY
CORRSET
100 Somaar
CORISOT
BALD
BALD
BROGE
B40G2
BADGE
8
00
Number of Selected Bowcs
Number Selected Boxts
2009
Number Sclectied Boxcs
0933
Figure 8: 3D mAP(%% of DDFH and AL baselines on the KITTI val split with SECOND.
comprehensive experimental results of DDFH on the KITTI Dataset in Fig. 7 and Fig. 8. The 19 results in Fig. 7 indicate that DDFH with PV-RCNN has significant advantage in all categories 20 in KITTI, consistent with the results of Figure in main text on SECOND. It is noteworthy that the car category, some uncertainty-based methods achieve similar performance DDFH with 22 the same annotation cost. However, these methods fail to improve effectively other categories, demonstrating DDFH's effectiveness in resource allocation and diversity. Fig. also provides the trend of average 3D mAP for the one-stage model SECOND in different difficulties, consistent with PV-RCNN, outperforming SOTA methods in all difficulties. Further, in Table 1, provide the performance of PV-RCNN trained on 100% labeled data, showing that DDFH's performance with only 1% bounding box annotation is close to fully trained performance, even outperforming fully trained models the pedestrian category.
18
21
28
27
28
We also test the stability and generalizability of DDFH through different density estimation methods and parameters. In Fig. 6b, we set different numbers of GMM components, specifically 1, 10 (DDFH Ours), 50, and 100. The results indicate that all experiments, except for component, maintain similar effectiveness. In Fig. 6c, we use Kernel 33 Density Estimation (KDE) to estimate the probability density and adjust different bandwidths to DDFH. Silverman [3] and Scott [4] calculate bandwidth 35 based on sample size. The results show that the performance of DDFH remains consistent and stable under different density estimation models and parameters. This is due to the distribution discrepancy focusing on distribution differences and novelty, rather than relying highly accurate distribution estimates, thus providing sufficient robustness to noisy instances and estimation deviations.
Ablation Study of Density Estimation.
31
32
34 test the stability and generalizability
C Limitation
Considering that the distribution of objects in real environments is often uneven, common objects tend to occupy the majority (e.g. cars). This leads to the underestimation of less frequent categories when estimating informativeness. Therefore, the components DD, FH, and CB DDFH reduce 42
41
44 tively balance minority categories. Although most real-world scenarios exhibit an uneven long-tail
45 distribution, if specific situations lead dataset where object distribution is close uniform distribution, the effectiveness of DDFH might be limited due to the less apparent distribution dif-
47 ferences. A possible solution is to incorporate indicators of uncertainty into DDFH, such as model
48 instability, entropy, or the kernel coding rate combined with KECOR. This approach could address 49 the mentioned limitation and left for future research.

---

References
50
51 [1] Y. Luo, Z. Chen, Z. Wang, X. Yu, Z. Huang, and M. Baktashmotlagh. Exploring active 3d object detection from a generalization perspective. In The Eleventh International Conference on Learn- ing Representations, 2023. URL https /tppr//v////eeeeeeeeemmam net/ j
52
58
[2] Y. Luo, Z. Chen, Z. Fang, Z. Zhang, M. Baktashmotlagh, and Z. Huang. Kecor: Kernel coding rate maximization for active 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18279-18290, 2023.
55
56
57
[3] B. W. Silverman. Density estimation for statisties and data analysis. 1986.
58
[4] D. W. Scott. Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, aug
59
1992. ISBN 1680470316849, 1oi:10.1002/97880 1101000//7707033164400 URL http::/dx.doi..org// 1002/9780470316649.
60
3


Reviews:
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The final information score is normalized to find the best data to annotate in future training for the largest data efficiency. Experiments have been conducted on different datasets to evaluate the proposed results.", "summary_of_recommendation": "Concerns about the soundness of the proposed method, some incomplete experiments", "technical_quality": 2, "clarity_of_presentation": 2, "potential_impact": 3, "robotics_focus": 3, "quality_of_limitations": 2, "questions_for_rebuttal": "See Review part.", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fnnzI8NZHv", "reviewer": "Reviewer_Ybjb", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 3, "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": "The paper introduces a novel active learning method called Distribution Discrepancy and Feature Heterogeneity (DDFH), which considers both geometric features and model embeddings to assess information from instance-level and frame-level perspectives. DDFH generates several scores, including discrepancy score, novelty score, correlation, and variance score. These multiple indicators are then aggregated using Quantile transform to provide a unified measure of informativeness. Extensive experiments demonstrate the benefits of the proposed approach, as it outperforms the current state-of-the-art methods on the KITTI and Waymo datasets.", "summary_of_recommendation": "I recommend a weak accept, as there is a definite use case for the proposed work. However, if there were an option for a borderline recommendation, I would have chosen that until clarity is provided for weaknesses 1 and 4.", "technical_quality": 4, "clarity_of_presentation": 3, "potential_impact": 3, "robotics_focus": 2, "quality_of_limitations": 1, "questions_for_rebuttal": "Please address the points listed under weaknesses, especially points 1 and 4.", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gGqyYuAoCs", "reviewer": "Reviewer_jH4f", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 3, "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": "The paper proposes a metric for active learning to choose a lidar frame for human annotation to maximize informativeness of the new labeled data with respect to existing labeled data. The proposed method uses three metrics to estimate informativeness: instance-level distribution discrepency, frame-level feature homogeneity, and confidence balance.", "summary_of_recommendation": "Great presentation and compelling results. Overall an excellent paper.", "technical_quality": 4, "clarity_of_presentation": 4, "potential_impact": 3, "robotics_focus": 2, "quality_of_limitations": 1, "questions_for_rebuttal": "Fix minor grammatical errors and add a limitations section.", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: Summary: This paper proposes DDFH, an active learning method for LiDAR-based 3D object detection that reduces annotation costs by 56.3% by simultaneously considering geometric features and model embeddings, ensuring feature diversity and efficient learning with limited data, outperforming current state-of-the-art methods on the KITTI and Waymo datasets.

Strengths:
* Proposes a novel active learning method (DDFH) for LiDAR-based 3D object detection, addressing challenges by enhancing instance distributional discrepancy and frame-feature heterogeneity.
* Outperforms state-of-the-art methods on KITTI and Waymo datasets.
* Includes extensive experiments and comparisons with multiple recent baselines, validating the proposed approach.

Weakness:
* Some key aspects, such as GMM-based distribution discrepancy and feature heterogeneity calculations, are not clearly explained and need more theoretical and empirical justification.
* Inconsistencies in the number of maximum selected boxes across different approaches in graphs make comparisons less clear.
* The method's performance on imbalanced classes like Cyclist and Pedestrian shows inconsistencies, with certain combinations decreasing performance.
* The paper does not discuss potential limitations or future research directions.

----

Post rebuttal: The reviewers are happy with the authors' replies and have reached a consensus on accepting the paper.
Decision: Accept