VEOcc++: Learning Persistent Voxel Feature Maps for Embodied Online Semantic Occupancy Mapping and Offline Refinement

Ruoyu Wang; Jiahan Li; Yong Liu; Kexin Zhang

cs.CVOct 20, 2026

VEOcc++: Learning Persistent Voxel Feature Maps for Embodied Online Semantic Occupancy Mapping and Offline Refinement

Authors: Ruoyu Wang, Jiahan Li, Yong Liu, Kexin Zhang

Organizations: APRIL Lab, Institute of Cyber-Systems and Control, Zhejiang University, China

Abstract

Semantic occupancy mapping supports embodied perception during exploration and detailed scene understanding afterward, giving rise to two complementary requirements: online incremental mapping for maintaining an up-to-date global map under causal constraints, and offline global map refinement for improving the completed scene representation with additional computation. We present VEOcc++, a unified voxel-centric framework that connects both stages through a persistent sparse voxel feature map. During exploration, the map expands with newly observed regions and integrates aligned local features through reliability-guided updates, while context-aware decoding produces the current semantic occupancy map. After exploration, cached local features are consolidated as complementary evidence and combined with the completed online representation for multi-scale spatial refinement. On EmbodiedOcc-ScanNet, VEOcc++ achieves 67.12% IoU and 58.40% mIoU, establishing new state-of-the-art performance for online incremental mapping. With the local predictor fixed, our online mapping pipeline outperforms VEOcc fusion by 3.70 IoU and 3.74 mIoU points, demonstrating substantial gains from the proposed global mapping design. Offline global map refinement further improves the completed map to 67.79% IoU and 59.37% mIoU using only the observations acquired during exploration. Zero-shot deployment on a mobile robot further demonstrates transferability to unseen environments, onboard feasibility, and scalability to large-scale indoor mapping. Project page: https://wryzju.github.io/VEOccpp/.

Figures & tables

Figure 1: Overview of the two-stage mapping setting in VEOcc++. Online incremental mapping sequentially integrates observations under causal constraints, while offline global map refinement exploits complementary local evidence and broader spatial context over the completed map. Zoomed examples highlight representative improvements after refinement.

Figure 2: Overall framework of VEOcc++. During online incremental mapping, local voxel features are aligned with the global map and accumulated in a Persistent Voxel Feature Map through reliability-guided updates, while Context-Aware Decoding produces the current occupancy map. During offline global map refinement, Local Evidence Aggregation consolidates cached local features as complementary evidence, which is combined with the completed online representation for Global Spatial Refinement.

Method	IoU	mIoU	$\blacksquare$ ceiling	$\blacksquare$ floor	$\blacksquare$ wall	$\blacksquare$ window	$\blacksquare$ chair	$\blacksquare$ bed	$\blacksquare$ sofa	$\blacksquare$ table	$\blacksquare$ tvs	$\blacksquare$ furniture	$\blacksquare$ objects
EmbodiedOcc ( Wu et al., 2025 )	53.95	45.48	40.90	50.80	41.90	33.00	41.20	55.20	61.90	43.80	35.40	53.50	42.90
EmbodiedOcc++ ( Wang et al., 2025 )	54.90	46.20	36.40	53.10	41.80	34.40	42.90	57.30	64.10	45.20	34.80	54.20	44.10
RoboOcc ( Zhang et al., 2025 )	56.48	47.67	45.36	53.49	44.35	34.81	43.38	56.93	63.35	46.35	36.12	55.48	44.78
TGSFormer ( Qian et al., 2026 )	64.42	54.73	54.20	61.30	50.50	41.30	49.50	64.50	69.30	52.40	47.10	63.00	48.80
GPOcc (VGGT) ( Zhou et al., 2026a )	63.14	56.19	51.67	59.93	52.07	46.44	51.35	64.45	69.47	54.30	51.76	63.29	53.36
GPOcc++ (VGGT) ( Zhou et al., 2026b )	63.72	57.41	56.62	61.40	50.63	48.13	53.12	66.34	71.05	56.48	48.95	64.48	54.22

Table 1: Local Occupancy Prediction results of embodied occupancy mapping methods on Occ-ScanNet. The best results are highlighted in bold , and the second-best results are underlined .

Method	IoU	mIoU	$\blacksquare$ ceiling	$\blacksquare$ floor	$\blacksquare$ wall	$\blacksquare$ window	$\blacksquare$ chair	$\blacksquare$ bed	$\blacksquare$ sofa	$\blacksquare$ table	$\blacksquare$ tvs	$\blacksquare$ furniture	$\blacksquare$ objects
EmbodiedOcc ( Wu et al., 2025 )	51.52	42.53	22.70	44.60	37.40	38.00	50.10	56.70	59.70	35.40	38.40	52.00	32.90
EmbodiedOcc++ ( Wang et al., 2025 )	52.20	43.60	27.90	43.90	38.70	40.60	49.00	57.90	59.20	36.80	37.80	53.50	34.10
RoboOcc ( Zhang et al., 2025 )	53.30	44.05	21.94	44.57	39.54	38.48	51.28	57.04	63.09	36.70	43.05	54.42	34.38
TGSFormer ( Qian et al., 2026 )	54.42	45.29	31.00	39.90	39.90	32.70	52.00	66.10	53.00	47.30	38.70	59.60	37.90
GPOcc (VGGT) ( Zhou et al., 2026a )	61.41	55.39	42.61	51.35	51.49	48.72	54.32	67.91	70.73	52.94	54.75	64.76	49.67
GPOcc++ (VGGT) ( Zhou et al., 2026b )	62.50	57.41	48.00	52.14	54.70	53.39	54.71	68.82	71.03	54.27	55.23	66.81	52.42

Table 2: Global Occupancy Mapping results on EmbodiedOcc-ScanNet. The best results are highlighted in bold , and the second-best results are underlined .

\dagger

denotes VEOcc using the same local predictor as VEOcc++.

Figure 3: Progressive online incremental mapping and offline map refinement. Each row shows the map evolution from $t=1$ to $t=T$ , followed by the refined result and ground truth. Red dashed boxes indicate the enlarged regions.

Figure 4: Qualitative comparison of online incremental mapping. Highlighted regions are enlarged for detailed comparison.

Figure 5: Zero-shot real-world mapping results. The upper and lower rows show the Reading Room and Laboratory Floor, respectively. Scene reconstructions are provided only for spatial reference and are not used by the occupancy predictor. The global maps demonstrate online incremental mapping at different scene scales, while offline refinement further improves the Reading Room result. A–C highlight representative local regions.

Setting	Online s/frame	Online s/scene	Offline s/scene	Online GPU GiB	Offline GPU GiB	Map B/voxel	Online IoU	Online mIoU	Offline IoU	Offline mIoU
FP32	0.152	4.57	0.319	1.753	3.169	424	67.12	58.40	67.79	59.37
Mixed FP16	0.120	3.60	0.224	1.558	3.252	232	67.08	58.36	67.77	59.35

Table 3: Efficiency and precision comparison on EmbodiedOcc-ScanNet. Runtime and GPU memory are measured on a single RTX 3090.

Scene	Travel Dist. (m)	Map Extent (m)	Key Frames	Online Avg. (s/frame)	Online Time (s/scene)	Offline Refine (s)	Online RSS (GiB)	Offline RSS (GiB)	Feature Map (MiB)	Local Cache (MiB)
Reading Room	31.31	$10.0\times 9.0$	91	0.630	57.32	4.29	4.172	4.340	35.07	151.55
Laboratory Floor	235.15	$57.3\times 15.6$	351	0.764	268.08	25.07	5.136	7.806	215.69	576.50

Table 4: Real-world deployment results in two indoor environments. Map extent denotes the horizontal bounding box of the final stored voxel map. RSS reports the peak process memory usage in each stage, while Local Cache denotes the cached local features stored for offline refinement.

Table 11

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Stage	Per-GPU Batch	Total Batch	Epochs	Training Time (h)
Local Predictor	4 images	16 images	10	24.8
Online Incremental Mapping	1 trajectory (30 frames)	4 trajectories	3	1.3
Offline Global Map Refinement	1 scene map	4 scene maps	30	1.9

Appendix

Table 7: Training configuration and time of the three stages.

Learning rate	Multiplier	IoU	mIoU
$4\times 10^{-4}$ (VEOcc; Wang et al., 2026 )	$1\times$	64.55	55.49
$8\times 10^{-4}$	$2\times$	66.11	57.35
$1.6\times 10^{-3}$	$4\times$	66.90	58.08
$2\times 10^{-3}$ (VEOcc++, Ours)	$5\times$	66.98	58.17

Appendix

Table 8: Local learning-rate study on Occ-ScanNet. All results are measured at epoch 10 using the same architecture and remaining training configuration. The best results are marked in bold .

Figure 7: Online deployment scaling on the Laboratory Floor sequence. We track runtime and memory as the global map grows over 351 motion-gated keyframes. The panels show per-keyframe online service latency, current process RSS, and Persistent Voxel Feature Map size, respectively. The centered rolling median over 21 keyframes highlights the latency trend, while the dashed line indicates the 1 s reference.

Figure 8: Qualitative comparison of online incremental mapping on real-world robotic sequences. The first two rows show self-collected sequences previously used in VEOcc, while the last two rows correspond to the two zero-shot deployment sequences reported in the main paper of VEOcc++. For each sequence, we compare the online maps produced by VEOcc and VEOcc++, and show representative local regions in the right column. VEOcc++ yields more complete structures, better continuity of extended objects and surfaces, and improved semantic consistency in the highlighted regions.

Semantic Occupancy Mapping

Semantic occupancy mapping predicts and maintains 3D maps of occupied, free, and unknown space, labeling objects and surfaces for perception, planning, and navigation.

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May 24, 2026J/K move · Enter open · S save

VEOcc: Voxel-Centric Online Semantic Occupancy Prediction For Embodied Scene Understanding

Ruoyu Wang, Yong Liu, Sheng Tao, Yuhang Lin+1

Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China.

Simultaneous Localization and Mapping Semantic Occupancy Mapping 3D Occupancy Prediction Embodied AI

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Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally constructs dense spatial representations on the fly. However, recent Gaussian-centric methods struggle with structural boundary fidelity and rely heavily on predefined scene-size priors, fundamentally limiting their operational efficiency. In this work, we present VEOcc, a voxel-centric framework formulated as a recursive perception-and-assimilation paradigm. By eliminating the need for initial scale estimation, VEOcc enables highly streamlined, open-ended map expansion. Furthermore, to robustly aggregate noisy temporal observations within the discrete voxel space, we propose a Spatio-Temporal-Aware Online Update Strategy. It integrates Cross-Temporal Logit Aggregation (TLA) for temporal consistency, Reliability-Aware Confidence Modulation (RCM) for spatial uncertainty calibration, and Confidence-Driven Incremental State Update (CSU) for robust global state assimilation. % Extensive experiments on Occ-ScanNet and EmbodiedOcc-ScanNet demonstrate that VEOcc establishes new state-of-the-art performance in both local and embodied settings, providing an accurate and efficient solution for real-world exploration. Extensive experiments on Occ-ScanNet and EmbodiedOcc-ScanNet demonstrate that VEOcc establishes new state-of-the-art performance in both local and embodied settings. Notably, zero-shot evaluations on self-collected video sequences further confirm its robust out-of-distribution generalization capability in completely unseen real-world environments. Ultimately, our framework provides an accurate and highly efficient solution for autonomous exploration. Code and supplementary visualizations are available on our project page: https://wryzju.github.io/VEOcc/.

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Semantic Occupancy Mapping

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