OPUS-V2: Bridging the Gap between Sparse Points and Dense Voxels

Jiabao Wang; Qiang Meng; Liujiang Yan; Ke Wang; Qibin Hou; Ming-Ming Cheng

cs.CVAug 29, 2026

OPUS-V2: Bridging the Gap between Sparse Points and Dense Voxels

Authors: Jiabao Wang, Qiang Meng, Liujiang Yan, Ke Wang, Qibin Hou, Ming-Ming Cheng

Organizations: Nankai University, Tianjin, 300350, China. · Momenta, Beijing, 102200, China. · KargoBot, Beijing, 102200, China.

Abstract

The point-based occupancy prediction paradigm has achieved an attractive trade-off between accuracy and efficiency by modeling 3D space sparsely. However, its predictions inherently mismatch the dense voxel-based occupancy required by self-driving systems, necessitating hand-crafted heuristics during training and inference that limit final performance. To overcome these limitations, we propose OPUS-V2, a novel framework built upon the pioneering OPUS (occupancy prediction using a sparse set) point-based approach. OPUS-V2 incorporates a lightweight point-voxel transformation (PVT) module behind the decoder to adaptively map sparse predictions into the dense voxel space, eliminating the need for suboptimal operations and improving model accuracy. Furthermore, our architecture decouples feature and occupancy generation processes, allowing OPUS-V2 to adapt to arbitrary occupancy resolutions. OPUS-V2 achieves a state-of-the-art rayIoU of 44.0 on the Occ3D dataset. On the more challenging OpenOccupancy dataset, it attains a competitive 16.4 mIoU while running in real time at 20.6 FPS.

Figures & tables

Table 1

Figure 1: Comparison of OPUS and OPUS-V2 pipelines. (a) OPUS needs suboptimal operations, such as a nearest-neighbor strategy in training and handcrafted post-processing in testing. In contrast, (b) OPUS-V2 leverages PVT to adaptively transform sparse predictions into the voxel space, facilitating direct interaction between predictions and ground truth.

Figure 2: Comparison between OPUS-V2 and previous methods on the Occ3D and OpenOccupancy datasets, evaluated using both mIoU and rayIoU metrics. FPS measurements were conducted on an A100 GPU with PyTorch FP32 precision, except for ALOcc [ 15 ] , for which FPS was measured on a 4090 GPU and is included for reference.

Figure 3: Numbers of mislabeled voxels in post-processing on the Occ3D dataset. We count mislabeled voxels whose occupancy status is correctly represented by predicted points but misclassified in post-processing.

Figure 4: OPUS-V2 employs a three-stage pipeline: (1) an image encoder to extract multi-view image features, (2) a series of decoders to produce 3D sparse points with corresponding latent features, and (3) a lightweight PVT module that adaptively transforms these sparse features into dense voxel space to produce the final occupancy predictions.

Figure 5: Training objectives in OPUS-V2. We separately supervise the sparse and dense predictions of decoders and PVT by the center points of occupied voxels and semantic labels of voxelized annotations.

Model	$Q$	$S$	$F$	#points in each stage
Model	$Q$	$S$	$F$	$R_{1}$	$R_{2}$	$R_{3}$	$R_{4}$	$R_{5}$
OPUS-V2-T	600	4	32	8	16	32	64	128
OPUS-V2-S	1200	2	32	4	8	16	32	64
OPUS-V2-M	2400	2	32	2	4	8	16	32
OPUS-V2-L	4800	2	32	1	2	4	8	16

Table 1: Configurations for different models.

Q

S

F

represent the numbers of queries, sampling points in each query, and channel size of predicted point features.

R_{i}

indicates the number of points predicted by a single query in the

i

-th decoder.

Methods	Input	Backbone	Image Size	Epoch	Vis. Mask	mIoU	$\text{rayIoU}_{\text{1m}}$	$\text{rayIoU}_{\text{2m}}$	$\text{rayIoU}_{\text{4m}}$	$\text{rayIoU}_{\text{}}$	FPS
RenderOcc [ 50 ]	C	Swin-B	$1408\times 512$	12	$\checkmark$	24.5	13.4	19.6	25.5	19.5	-
BEVFormer [ 51 ]	C	R101	$1600\times 900$	24	$\checkmark$	39.3	26.1	32.9	38.0	32.4	3.0
BEVDet-Occ [ 52 ]	C	R50	$704\times 256$	90	$\checkmark$	36.1	23.6	30.0	35.1	29.6	2.6
BEVDet-Occ (8f) [ 52 ]	C	R50	$704\times 384$	90	$\checkmark$	39.3	26.6	33.1	38.2	32.6	0.8
FB-Occ (16f) [ 4 ]	C	R50	$704\times 256$	80 ∗	$\checkmark$	39.1	26.7	34.1	39.7	33.5	10.3
SparseOcc (8f) [ 7 ]	C	R50	$704\times 256$	12	-	30.9	28.0	34.7	39.4	34.0	17.3

Table 2: Occupancy prediction performance on the Occ3D dataset. The best result in each column is highlighted in bold. Models using temporal fusion over 8 and 16 frames are denoted by ‘8f’ and ‘16f,’ respectively. Some models implement CBGS [ 49 ] , which extends the training duration by nearly 4.5 times (marked with ∗ ). All FPS results are sourced from their corresponding papers. Most were measured on an A100 GPU unless otherwise noted; ALOcc (marked with † ) was measured on an RTX 4090.

Method	Input	IoU	mIoU	barrier	bicycle	bus	car	const. veh.	motorcycle	pedestrian	traffic cone	trailer	truck	drive. suf.	other flat	sidewalk	terrain	manmade	vegetation	FPS
MonoScene [ 3 ]	C	18.4	6.9	7.1	3.9	9.3	7.2	5.6	3.0	5.9	4.4	4.9	4.2	14.9	6.3	7.9	7.4	10.0	7.6	-
TPVFormer [ 6 ]	C	15.3	7.8	9.3	4.1	11.3	10.1	5.2	4.3	5.9	5.3	6.8	6.5	13.6	9.0	8.3	8.0	9.2	8.2	-
C-CONet [ 2 ]	C	20.1	12.8	13.2	8.1	15.4	17.2	6.3	11.2	10.0	8.3	4.7	12.1	31.4	18.8	18.7	16.3	4.8	8.2	-
SparseOcc [ 48 ]	C	21.8	14.1	16.1	9.3	15.1	18.6	7.3	9.4	11.2	9.4	7.2	13.0	31.8	21.7	20.7	18.8	6.1	10.6	-
3DSketch [ 56 ]	C&D	25.6	10.7	12.0	5.1	10.7	12.4	6.5	4.0	5.0	6.3	8.0	7.2	21.8	14.8	13.0	11.8	12.0	21.2	-
AICNet [ 57 ]	C&D	23.8	10.6	11.5	4.0	11.8	12.3	5.1	3.8	6.2	6.0	8.2	7.5	24.1	13.0	12.8	11.5	11.6	20.2	-

Table 3: Occupancy prediction performance on the OpenOccupancy dataset. The best result in each column is highlighted in bold. The results of previous methods are taken from the papers of OpenOccupancy and SparseOcc proposed by Tang et al. FPS results of OPUS-V2 are measured on an A100 GPU.

models	Image Size	IoU	mIoU	Mem. (GB)	FPS
SparseOcc [ 48 ]	$704\times 256$	21.8	14.1	13.0	-
SparseOcc [ 48 ]	$1600\times 900$	20.4	14.6	-	-
OPUS-V2-T	$704\times 256$	24.8	15.1	2.3	24.2
OPUS-V2-T	$1600\times 900$	26.2	15.7	11.6	6.9

Table 4: Comparison of the sparse voxel-based method for different input image resolutions. The results of SparseOcc by Tang et al. were obtained from their original paper. FPS results were measured on a RTX 4090 GPU.

index	NN	PVT	$\text{rayIoU}_{\text{1m}}$	$\text{rayIoU}_{\text{2m}}$	$\text{rayIoU}_{\text{4m}}$	$\text{rayIoU}_{\text{}}$
(I)	✓		30.3	38.0	43.1	37.2
(II)	✓	✓	34.0	41.3	46.2	40.6
(III)		✓	34.6	41.7	46.5	41.0

Table 5: Ablation studies for different training and testing strategies. ‘NN’ means using the proxy constructed by the nearest-neighbor in classification training. ‘PVT’ means employing the PVT module in model.

$\mathbf{f}$	$\mathbf{p}_{\text{c}}$	$\mathbf{p}_{\text{v}}$	$\text{rayIoU}_{\text{1m}}$	$\text{rayIoU}_{\text{2m}}$	$\text{rayIoU}_{\text{4m}}$	$\text{rayIoU}_{\text{}}$
✓			33.6	41.0	45.9	40.2
✓	✓		34.5	41.6	46.5	40.9
✓	✓	✓	34.6	41.7	46.5	41.0

Table 6: Ablation studies for components in point features.

\mathbf{f}

denotes the semantic features predicted by decoders;

\mathbf{p}_{\text{c}}

and

\mathbf{p}_{\text{v}}

denote relative positions from corresponding query centers and voxel centers, respectively.

Figure 6: Varying the number of decoders. All models were trained for 50 epochs. FPS results were measured on an A100 GPU.

Figure 7: Convergence of focal loss and chamfer distance loss with weights of chamfer distance loss set to 0.5 and 1.0.

Figure 8: Visualizations of the sparse point-based and dense voxel-based predictions of OPUS-V2.

Figure 9: Results of the original OPUS and OPUS-V2 trained on Occ3D and OpenOccupancy.

3D Occupancy Prediction

3D occupancy prediction estimates the geometry and semantics of occupied and free space around an agent from camera, LiDAR, or radar data.

See all 114 papers

SparseOcc++: Geometry-Aware Sparse Latent Representation for Semantic Occupancy Prediction
Jul 6, 2026Pin Tang, Zhongdao Wang, Guoqing Wang +23D Reconstruction Autonomous Driving Perception
MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China · Huawei Noah’s Ark Lab, China
Streaming Dense Voxel Representations for 3D Occupancy Prediction
Mar 28, 2025Seokha Moon, Janghyun Baek, Yujin Jeong +53D Occupancy Prediction 3D Representation Learning
1Korea University · 5NAVER LABS · † Work done during an internship at NAVER LABS. +4
Semantic Occupancy Prediction with Dual Range-Voxel Representation
Jun 30, 2026Sitao Chen, Zhuangwei Zhuang, Hui Luo +3Autonomous Driving Perception Semantic Occupancy Mapping
School of Software Engineering, South China University of Technology, Guangzhou 510006, China · State Key Laboratory of Optical Field Manipulation Science and Technology, Institute of Optics and Electronics, CAS, Chengdu 610209, China

Jul 6, 2026J/K move · Enter open · S save

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

3D Occupancy Prediction

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