$M^2$-Occ: Resilient 3D Semantic Occupancy Prediction for Autonomous Driving with Incomplete Camera Inputs

Kaixin Lin; Kunyu Peng; Di Wen; Yufan Chen; Ruiping Liu; Kailun Yang

cs.CVMar 10, 2026

$M^2$ -Occ: Resilient 3D Semantic Occupancy Prediction for Autonomous Driving with Incomplete Camera Inputs

Authors: Kaixin Lin, Kunyu Peng, Di Wen, Yufan Chen, Ruiping Liu, Kailun Yang

Abstract

Semantic occupancy prediction enables dense 3D geometric and semantic understanding for autonomous driving. However, existing camera-based approaches implicitly assume complete surround-view observations, an assumption that rarely holds in real-world deployment due to occlusion, hardware malfunction, or communication failures. We study semantic occupancy prediction under incomplete multi-camera inputs and introduce $M^2$ -Occ, a framework designed to preserve geometric structure and semantic coherence when views are missing. $M^2$ -Occ addresses two complementary challenges. First, a Multi-view Masked Reconstruction (MMR) module leverages the spatial overlap among neighboring cameras to recover missing-view representations directly in the feature space. Second, a Feature Memory Module (FMM) introduces a learnable memory bank that stores class-level semantic prototypes. By retrieving and integrating these global priors, the FMM refines ambiguous voxel features, ensuring semantic consistency even when observational evidence is incomplete. We introduce a systematic missing-view evaluation protocol on the nuScenes-based SurroundOcc benchmark, encompassing both deterministic single-view failures and stochastic multi-view dropout scenarios. Under the safety-critical missing back-view setting, $M^2$ -Occ improves the IoU by 4.36%. As the number of missing cameras increases, the robustness gap further widens; for instance, under the setting with five missing views, our method boosts the IoU by 6.67%. These gains are achieved without compromising full-view performance. The source code will be publicly released at https://github.com/qixi7up/M2-Occ.

Figures & tables

Fig. 1 : Existing methods fail with missing camera inputs ( e.g. , FRONT-view). Our $M^{2}$ -Occ reconstructs missing features from adjacent overlaps and stabilizes semantics via global memory, achieving superior performance.

Fig. 2 : Overview of $M^{2}$ -Occ. Multi-view images are processed by a shared backbone. MMR reconstructs missing views from adjacent overlaps. Features are lifted to a unified 3D volume. FMM refines voxel features via global semantic prototypes before generating dense 3D occupancy.

Fig. 3 : An overview of Multi-view Masked Reconstruction (MMR). The MMR module extracts overlapping boundary features from adjacent unmasked views and concatenates them with a central learnable mask token. A lightweight transformer decoder then processes this structural prior to reconstruct the missing view’s representations, preserving spatial continuity.

Fig. 4 : A comparison between the single-proto strategy and the multi-proto strategy. While the single-proto approach maintains one global centroid per semantic class, the multi-proto strategy captures intra-class variance by learning multiple sub-prototypes and dynamically retrieving them based on feature similarity.

Setting	Method	IoU	mIoU	barrier	bicycle	bus	car	const.veh.	motorcycle	pedestrian	traffic cone	trailer	truck	drive.surf.	other flat	sidewalk	terrain	manmade	vegetation
Standard	SurroundOcc (Wei et al. 2023 [ 7 ] )	31.18	17.23	16.16	8.15	19.07	29.28	10.22	9.11	10.59	8.19	11.09	18.54	38.04	19.20	23.35	21.57	12.68	20.44
Standard	Ours (SurroundOcc)	31.17	18.03	17.56	7.65	23.86	29.54	9.92	12.63	11.31	7.96	12.03	20.31	38.78	18.29	24.10	21.42	12.63	20.56
Front	SurroundOcc (Wei et al. 2023 [ 7 ] )	23.96	13.35	13.90	7.83	15.91	25.02	9.90	6.01	8.93	8.04	9.55	16.77	14.56	14.12	16.41	17.39	11.34	17.96
Front	Ours (SurroundOcc)	29.30	15.19	14.64	4.03	18.64	25.26	9.40	7.48	7.41	6.92	10.27	17.56	35.35	14.09	21.57	19.24	11.91	19.33
Front Right	SurroundOcc (Wei et al. 2023 [ 7 ] )	29.89	15.92	15.12	7.28	17.66	27.23	8.65	8.21	9.40	7.49	10.29	17.43	36.58	17.82	21.45	19.29	11.80	19.01
Front Right	Ours (SurroundOcc)	30.34	17.02	16.32	6.99	23.64	27.58	8.71	11.75	9.83	7.38	11.03	19.26	37.79	17.31	23.03	20.38	11.82	19.54

TABLE I : Single-view missing results on SurroundOcc (IoU, mIoU, as well as per-category voxel-level IoU).

Setting	Method	mIoU (vox)	mIoU (pts)	barrier	bicycle	bus	car	const.veh.	motorcycle	pedestrian	traffic cone	trailer	truck	drive.surf.	other flat	sidewalk	terrain	manmade	vegetation
Standard	TPVFormer (Huang et al. 2023 [ 8 ] )	47.84	26.92	35.07	12.71	48.70	48.51	29.52	20.04	19.61	8.90	38.32	46.09	15.45	13.82	19.82	20.83	27.29	25.99
Standard	Ours (TPVFormer)	50.26	30.11	36.42	11.30	50.71	53.86	35.98	32.90	28.90	10.62	41.51	53.28	17.41	12.75	19.18	21.45	28.49	27.06
Front	TPVFormer (Huang et al. 2023 [ 8 ] )	43.82	25.45	34.17	12.04	39.19	47.20	29.71	17.60	17.41	8.50	32.28	44.52	18.04	13.42	19.66	21.73	26.94	24.80
Front	Ours (TPVFormer)	46.89	27.94	34.43	10.92	40.95	52.07	34.78	32.18	27.38	10.33	36.86	48.97	16.44	11.32	17.39	20.69	27.41	24.98
Front Right	TPVFormer (Huang et al. 2023 [ 8 ] )	42.00	23.48	32.16	11.96	43.43	45.64	20.79	17.72	13.84	8.36	33.24	40.37	14.72	11.28	16.55	19.07	23.48	23.06
Front Right	Ours (TPVFormer)	44.36	26.09	31.40	10.33	45.91	49.06	26.16	23.61	26.88	9.35	35.34	45.71	16.48	10.25	17.21	20.45	25.12	24.15

TABLE II : Single-view missing results on TPVFormer (voxel-level and point-level mIoU, as well as per-category IoU).

Missing Views	Method	IoU (%)	mIoU (%)
0	Baseline	31.18	17.23
1	Baseline	27.70	14.95
	+ MMR	29.70	15.75
	+ MMR + FMM	29.77	16.25
3	Baseline	20.00	10.04
	+ MMR	26.09	11.11

TABLE III : Robustness analysis under different numbers of missing views. We compare the baseline, our method with MMR only, and our full model with both MMR and FMM.

Missing	MMR	FMM	IoU	mIoU
$\times$	$\times$	$\times$	31.18	17.23
✓	$\times$	$\times$	27.70	14.95
✓	✓	$\times$	29.70	15.75
✓	✓	✓	29.77	16.25

TABLE IV : Ablation study on the modules in the presented framework.

Fig. 5 : Visualizations on the nuScenes validation set [ 2 ] . Our method achieves promising results in the reconstruction of the missing $F$ view across various scenarios, even under weak lighting conditions.

Fig. 6 : Visualization of feature maps. Our reconstructed features can largely replace the original features.

Method	Number of Missing Views	Latency(s)	Memory(G)
Baseline	-	0.50	5.927
Ours	1	0.77	6.077
	2	0.91
	3	1.00
	4	1.11
	5	1.25

TABLE V : Experimental results of computation overhead.

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SparseOcc++: Geometry-Aware Sparse Latent Representation for Semantic Occupancy Prediction

Pin Tang, Zhongdao Wang, Guoqing Wang, Xiangxuan Ren+1

MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China · Huawei Noah’s Ark Lab, China

3D Reconstruction Autonomous Driving Perception 3D Semantic Segmentation Semantic Occupancy Mapping 3D Occupancy Prediction 3D Scene Completion

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Vision-based 3D semantic occupancy prediction is essential for autonomous driving, yet dense voxel representations waste computation on largely empty space, while BEV and TPV projections compromise fine-grained 3D structure. Fully sparse representations offer an attractive alternative, but existing methods, including SparseOcc, entangle scene completion with semantic prediction by indiscriminately propagating high-dimensional features into empty regions and applying voxel-wise classification. This creates excessive activations, computational overhead, and geometric ambiguity. We present SparseOcc++, a geometry-aware sparse framework that explicitly decouples scene completion from semantic segmentation. SparseOcc++ reformulates completion as signed-distance regression on sparse anchor voxels through a scene completion field (SCF). To model complex outdoor geometry robustly, it combines orthogonal decomposition with discretized distance learning. A geometry-guided propagation module then converts the SCF into a complete volumetric scene and restricts semantic segmentation to geometrically verified regions. Experiments establish new state of the art: SparseOcc++ improves IoU by 2.3 points and is 3.9x faster than SparseOcc on nuScenes, while achieving a 5.9x speedup over OccFormer on SemanticKITTI.

Abstract

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