AquaBEV: Monocular Underwater BEV Occupancy with 3D Sonar Supervision

Trung Tien Dong; Shengji Jin; Chen Chen; Yi Sheng; Xiaomin Lin

cs.ROSep 3, 2026

AquaBEV: Monocular Underwater BEV Occupancy with 3D Sonar Supervision

Authors: Trung Tien Dong, Shengji Jin, Chen Chen, Yi Sheng, Xiaomin Lin

Organizations: ERA Lab, University of South Florida, Tampa, Florida, USA. · YES Lab, University of South Florida, Tampa, Florida, USA. · University of Central Florida, Orlando, Florida, USA.

Abstract

Autonomous underwater robots are widely used for exploration, monitoring, and inspection, where safe navigation depends on understanding the surrounding free and occupied space. Bird's eye view (BEV) occupancy provides such a representation, but predicting it from a single underwater RGB image is difficult due to limited, unreliable geometric cues from appearance alone. 3D imaging sonar offers complementary geometric measurements to supervise this task. We introduce AquaBEV, a monocular underwater occupancy model that predicts local BEV occupancy from a single RGB image, using paired 3D imaging sonar as geometric supervision during training. AquaBEV maps visual features into a calibration free polar representation and applies causal decoding along the range dimension before reconstructing the prediction in Cartesian BEV coordinates. A controlled underwater occupancy benchmark was established, adapting representative occupancy methods to the same RGB to sonar task under a unified protocol. AquaBEV achieves 31.4 Visible IoU and 38.6 Observed IoU, 4.0% and 4.3% relative improvements over the strongest transferred baseline.

Figures & tables

Figure 1: A single RGB image is used to predict local BEV occupancy.

Figure 2: Overview of AquaBEV. AquaBEV encodes a single RGB image into a learned polar representation ( $\theta$ , r), applies causal reasoning along the range dimension, and differentiably transforms the resulting polar occupancy prediction into Cartesian BEV coordinates (x, y).

Method	Design		Occupancy		Range				Geometry
Method	Representation	Geometry	Visible $\uparrow$	Observed $\uparrow$	Near $\uparrow$	Mid $\uparrow$	Far $\uparrow$	R Macro $\uparrow$	bF1@2 $\uparrow$	Chamfer $\downarrow$
SurroundOcc [ 27 ]	Dense voxel	Projection based	29.8	34.9	38.9	40.6	18.9	32.8	48.4	0.355
BEVFormer [ 19 ]	BEV queries	Projection based	28.7	34.6	39.7	39.5	18.4	32.6	45.1	0.398
VoxFormer [ 18 ]	Sparse voxel	Projection based	29.2	35.7	38.8	39.6	19.7	32.7	47.0	0.371
MonoScene [ 3 ]	Dense voxel	Projection based	29.8	35.8	40.8	39.7	20.3	33.6	45.4	0.381
TPVFormer [ 13 ]	Tri perspective	Projection based	29.0	34.2	37.4	39.3	18.9	31.8	47.2	0.382

Table 1: Comparison of monocular underwater BEV occupancy methods on the proposed benchmark. All methods use the same RGB input, three state sonar supervision, data split, ConvNeXt B backbone, training schedule, and evaluation protocol ( BEST / SECOND BEST ).

Method	Vis. $\uparrow$	Obs. $\uparrow$	Near $\uparrow$	Mid $\uparrow$	Far $\uparrow$	R Macro $\uparrow$	bF1@2 $\uparrow$	Chamfer $\downarrow$
Cartesian decoder	30.8	37.4	42.4	42.0	20.7	35.0	47.7	0.351
Polar decoder	31.3	37.5	42.3	42.0	21.5	35.2	48.0	0.342
Polar + Survival supervision	31.3	38.5	42.6	42.0	21.7	35.7	45.8	0.344
Polar + Metric angular coupling	31.0	38.4	42.5	42.0	21.5	35.3	46.2	0.349
Polar + Full range context	30.9	38.6	41.1	42.0	22.1	35.0	44.1	0.360
AquaBEV	31.4	38.6	43.0	42.1	22.2	35.8	45.9	0.340

Table 2: Ablation of AquaBEV on occupancy and geometric quality. All results are averaged over three seeds.

Encoder	Params.	Val.	Vis. $\uparrow$	Near $\uparrow$
DINOv2 S	23.2M	30.6	28.2	39.0
ResNet 50	25.6M	33.9	32.7	45.3
ConvNeXt T	29.5M	33.5	30.9	42.6
ResNet 101	44.6M	33.6	30.1	42.1
ConvNeXt S	51.2M	33.5	29.1	40.1
ResNet 152	60.3M	33.8	31.3	43.9

Table 3: RGB encoder ablation. All encoders are evaluated using the same calibration free occupancy framework, training protocol, and held out test session. The results are from one seed.

Figure 3: Qualitative comparison of monocular underwater BEV occupancy.

Figure 4: Ablation comparison of Casual Range Decoder.

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AquaBEV-Nav: Learned BEV Occupancy for Underwater Navigation and Exploration

Trung Tien Dong, Zhenqi Wu, Sahasra Kondapalli, Jiayi Wu+2

1ERA Lab, University of South Florida, Florida, USA · University of Maryland- College Park, College Park, Maryland · 2YES Lab, University of South Florida, Florida, USA

Autonomous Underwater Navigation Underwater Robotics 3D Occupancy Prediction

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Safe underwater exploration requires a robot to understand where surrounding structures are located and which regions are available for motion. Existing vision-based underwater exploration systems commonly obtain this information indirectly by estimating monocular depth, unprojecting the geometry into 3D space, and accumulating it into a 2D bird's-eye-view occupancy map. This reliance on intermediate depth estimation is particularly problematic underwater, where scattering and wavelength-dependent attenuation degrade visual cues and limit the reliability of monocular depth estimates. We introduce AquaBEV-Nav, an underwater exploration framework that bypasses explicit monocular depth estimation through direct bird's-eye-view occupancy prediction. Built upon the CORAL hierarchical exploration framework, AquaBEV-Nav replaces its depth-based perception front end with AquaBEV. Given a single RGB frame, AquaBEV maps visual features into a learned polar representation, performs causal reasoning along the range dimension, and reconstructs local Cartesian occupancy without relying on intermediate depth prediction. The resulting occupancy map is accumulated into CORAL's persistent spatial memory, providing spatial context for VLM-based high-level planning and collision constraints for dynamics-aware local trajectory generation. Across ten simulated reef environments and six occupancy backbones evaluated under a single protocol, AquaBEV-Nav reaches 37.48 structure IoU and 53.2 target IoU, 88.95% closed-loop coverage with zero collisions.

Abstract

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