Class-Distribution Guided Active Learning for 3D Occupancy Prediction in Autonomous Driving

Wonjune Kim; In-Jae Lee; Sihwan Hwang; Sanmin Kim; Dongsuk Kum

cs.CVMar 28, 2026

Class-Distribution Guided Active Learning for 3D Occupancy Prediction in Autonomous Driving

Authors: Wonjune Kim, In-Jae Lee, Sihwan Hwang, Sanmin Kim, Dongsuk Kum

Organizations: Robotics Program, KAIST, Daejeon 34141, South Korea · Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, South Korea · Graduate School of Mobility, KAIST, Daejeon 34051, South Korea · Department of Automobile and IT Convergence, Kookmin University, Seoul 02707, South Korea

Abstract

3D occupancy prediction provides dense spatial understanding critical for safe autonomous driving. However, this task suffers from a severe class imbalance due to its volumetric representation, where safety-critical objects (bicycles, traffic cones, pedestrians) occupy minimal voxels compared to dominant backgrounds. Additionally, voxel-level annotation is costly, yet dedicating effort to dominant classes is inefficient. To address these challenges, we propose a class-distribution guided active learning framework for selecting training samples to annotate in autonomous driving datasets. Our approach combines three complementary criteria to select the training samples. Inter-sample diversity prioritizes samples whose predicted class distributions differ from those of the labeled set, intra-set diversity prevents redundant sampling within each acquisition cycle, and frequency-weighted uncertainty emphasizes rare classes by reweighting voxel-level entropy with inverse per-sample class proportions. We ensure evaluation validity by using a geographically disjoint train/validation split of Occ3D-nuScenes, which reduces train-validation overlap and mitigates potential map memorization. With only 42.4% labeled data, our framework reaches 26.62 mIoU, comparable to full supervision and outperforming active learning baselines at the same budget. We further validate generality on SemanticKITTI using a different architecture, demonstrating consistent effectiveness across datasets.

Figures & tables

Fig. 1: Overview of the proposed class-distribution guided acquisition strategy based on predictive uncertainty and class-distribution diversity. Unlabeled samples receive an uncertainty score and a diversity score computed from similarity to the current selection and to the labeled set. The method selects samples that are uncertain and dissimilar to both sets and rejects redundant or certain samples. Sample 1 is selected because uncertainty is high and similarity to both sets is low. Sample 2 is rejected because it is similar to the selected Sample 1, which lowers intra-set diversity. Sample 3 is rejected because it is similar to the labeled reference Sample 4 and its uncertainty is low. Sample 4 is the labeled anchor used for similarity assessment. The bars show predicted class distributions.

Fig. 2: Overview of our active learning method for 3D occupancy prediction. An initial labeled set trains the model, which then performs inference on the unlabeled pool. In each iteration, candidate samples are scored using three complementary metrics: frequency-weighted uncertainty, inter-sample diversity relative to the labeled set, and intra-set diversity within the current selected samples. Scores are normalized and combined into a unified acquisition score. Top-ranked samples are selected for annotation and added to the labeled subset. The process repeats for $T$ cycles, each time updating the labeled set and retraining the model.

Cycle ( $N$ samples, %)	AL Method	mIoU	$\blacksquare$ others	$\blacksquare$ barrier	$\blacksquare$ bicycle	$\blacksquare$ bus	$\blacksquare$ car	$\blacksquare$ const. veh.	$\blacksquare$ motorcycle	$\blacksquare$ pedestrian	$\blacksquare$ traffic cone	$\blacksquare$ trailer	$\blacksquare$ truck	$\blacksquare$ drive. surf.	$\blacksquare$ other flat	$\blacksquare$ sidewalk	$\blacksquare$ terrain	$\blacksquare$ manmade	$\blacksquare$ vegetation
Cycle 1 (2,000, 8.5%)	Random	18.33	0.25	30.48	0.00	23.79	37.11	0.15	0.00	6.53	0.00	11.61	23.93	68.64	8.85	30.47	31.99	21.20	16.52
Cycle 2 (4,000, 17.0%)	Random	23.35	1.31	36.37	0.92	33.18	40.03	10.43	3.95	14.52	10.12	26.03	28.42	70.54	11.82	33.03	34.60	22.31	19.33
	Entropy	22.70	2.01	35.08	0.33	32.24	39.04	7.94	7.98	13.79	7.89	23.61	27.57	70.13	10.87	32.48	34.25	22.23	18.49
	Coreset [ 13 ]	22.69	2.02	35.77	0.04	31.26	39.70	10.77	1.40	12.96	8.90	23.11	28.25	70.37	11.45	33.28	34.68	22.55	19.20
	Ours	24.17	2.75	35.72	0.81	34.04	40.15	16.84	5.00	14.51	12.78	25.94	30.21	70.32	12.43	33.85	34.22	22.40	18.99
Cycle 3 (6,000, 25.4%)	Random	24.40	3.38	35.88	0.89	32.87	40.21	13.64	8.84	14.45	12.04	27.01	28.83	71.02	12.50	34.26	35.43	23.20	20.30

TABLE I: Performance Comparison of Active Learning Methods on the Occ3D-nuScenes validation set [ 4 ] , constructed using a geographically disjoint split (train/val non-overlapping) following the protocol of Lilja et al. [ 17 ] . ‘Const. Veh.’ denotes Construction Vehicles, while ‘Drive. Surf.’ represents Drivable Surfaces.

AL Method	Cycle 1 (7.8%)	Cycle 2 (15.6%)	Cycle 3 (23.4%)	Cycle 4 (31.2%)	Cycle 5 (39.0%)	Fully Sup.
Random	9.66	10.13	10.50	10.69	11.17	13.46
Entropy		10.32	10.81	11.24	11.35
Coreset [ 13 ]		10.74	11.25	11.74	11.77
Ours		10.79	11.82	12.23	12.51

TABLE II: Cycle-wise Semantic Scene Completion (SSC) mIoU on the SemanticKITTI [ 8 ] validation set using OccFormer [ 6 ] . Cumulative labeled ratios at each cycle are shown in parentheses.

Fig. 3: Qualitative comparison of active learning strategies for 3D occupancy prediction. Each example shows six surround-view images and the ground truth occupancy grid, followed by model outputs when samples are chosen by random, entropy, Coreset [ 13 ] , and our active learning methods. Red dashed boxes mark regions where our approach more accurately predicts occupancy than the baselines. This improvement comes from our sample selection strategy, which emphasizes rare-class uncertainty and diversity in sample-level class distributions.

AL Method	Night ( mIoU / #samples )				Rainy ( mIoU / #samples )
	C2	C3	C4	C5	C2	C3	C4	C5
Random	13.54	14.31	14.54	14.23	24.96	26.33	27.55	26.88
	229	214	215	237	355	406	402	339
Entropy	14.74	14.48	14.26	14.37	24.44	25.96	26.64	27.13
	795	525	149	275	464	337	374	407
Coreset [ 13 ]	13.10	13.76	13.91	14.07	24.65	26.38	26.66	27.41

TABLE III: Cycle-wise mIoU on the Occ3D-nuScenes geographically disjoint validation split, evaluated on the Night and Rainy subsets. C2–C5 correspond to Cycle 2–5. Values show mIoU (top) and number of selected samples ( #samples , bottom). Best is in bold .

Fig. 4: Frequency-weighted uncertainty visualization. Two representative training samples are shown. Top: A high-uncertainty sample with diverse classes, including rare objects. Bottom: A low-uncertainty sample dominated by common classes. Frequency weighting highlights rare-class regions and suppresses dominant backgrounds. Dashed boxes indicate zoomed-in regions.

$U_{\mathrm{fw}}$	$D_{\mathrm{inter}}$	$D_{\mathrm{intra}}$	mIoU (%)
$U_{\mathrm{fw}}$	$D_{\mathrm{inter}}$	$D_{\mathrm{intra}}$	Cycle 2	Cycle 3	Cycle 4	Cycle 5
			23.35	24.40	25.84	25.67
✓			23.55	24.93	26.00	26.15
	✓		23.54	24.65	25.99	25.96
		✓	23.42	24.70	25.96	26.08
✓	✓		23.48	24.82	25.98	26.10

TABLE IV: Ablation study of frequency-weighted uncertainty (

U_{\mathrm{fw}}

), inter-sample diversity (

D_{\mathrm{inter}}

), and intra-set diversity (

D_{\mathrm{intra}}

) across active learning cycles on the Occ3D-nuScenes geographically disjoint split validation set.

Init. Size	mIoU $N_{L}$
Init. Size	Cycle 1	Cycle 2	Cycle 3	Cycle 4	Cycle 5
2,000	18.33 2k	24.17 4k	25.57 6k	26.38 8k	26.62 10k
4,000	20.12 4k	23.77 6k	25.23 8k	26.19 10k	26.63 12k
6,000	22.94 6k	25.22 8k	25.63 10k	26.18 12k	26.60 14k
8,000	24.36 8k	25.81 10k	26.12 12k	26.40 14k	26.66 16k
Fully sup.	26.83 23.5k

TABLE V: Cycle-wise mIoU (%) for different initial labeled set sizes on the Occ3D-nuScenes geographically disjoint split validation set (FlashOCC). Gray superscripts denote cumulative labeled samples (

{\color[rgb]{0.5,0.5,0.5}N_{L}}

), where

{\color[rgb]{0.5,0.5,0.5}k}

denotes thousands.

Explore similar work

ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction
Apr 1, 2026Yuheng Zhang, Mengfei Duan, Kunyu Peng +53D Occupancy Prediction OOD Detection
Hunan University · Karlsruhe Institute of Technology · INSAIT, Sofia University “St. Kliment Ohridski”
Dr.Occ: Depth- and Region-Guided 3D Occupancy from Surround-View Cameras for Autonomous Driving
Mar 1, 2026Xubo Zhu, Haoyang Zhang, Fei He +43D Occupancy Prediction Autonomous Driving Perception
School of Electronic Information, Wuhan University · Horizon Robotics · Wuhan University Shenzhen Research Institute
An Instance-Centric Panoptic Occupancy Prediction Benchmark for Autonomous Driving
Mar 28, 2026Yi Feng, Junwu E, Zizhan Guo +33D Occupancy Prediction Autonomous Driving Benchmarks
Tongji University

Apr 1, 2026J/K move · Enter open · S save

ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction

Yuheng Zhang, Mengfei Duan, Kunyu Peng, Yuhang Wang+4

Hunan University · Karlsruhe Institute of Technology · INSAIT, Sofia University “St. Kliment Ohridski”

3D Occupancy Prediction OOD Detection Prototype Learning

Open paper AskPDF arXiv

3D semantic occupancy prediction is central to autonomous driving, yet current methods are vulnerable to long-tailed class bias and out-of-distribution (OOD) inputs, often overconfidently assigning anomalies to rare classes. We present ProOOD, a lightweight, plug-and-play method that couples prototype-guided refinement with training-free OOD scoring. ProOOD comprises (i) prototype-guided semantic imputation that fills occluded regions with class-consistent features, (ii) prototype-guided tail mining that strengthens rare-class representations to curb OOD absorption, and (iii) EchoOOD, which fuses local logit coherence with local and global prototype matching to produce reliable voxel-level OOD scores. Extensive experiments on five datasets demonstrate that ProOOD achieves state-of-the-art performance on both in-distribution 3D occupancy prediction and OOD detection. On SemanticKITTI, it surpasses baselines by +3.57% mIoU overall and +24.80% tail-class mIoU; on VAA-KITTI, it improves AuPRCr by +19.34 points, with consistent gains across benchmarks. These improvements yield more calibrated occupancy estimates and more reliable OOD detection in safety-critical urban driving. The source code is publicly available at https://github.com/7uHeng/ProOOD.

Abstract

Explore similar work

ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction

Dr.Occ: Depth- and Region-Guided 3D Occupancy from Surround-View Cameras for Autonomous Driving

An Instance-Centric Panoptic Occupancy Prediction Benchmark for Autonomous Driving