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PANORAMA: The Rise of Omnidirectional Vision in the Embodied AI Era

Xu Zheng, Chenfei Liao, Ziqiao Weng, Kaiyu Lei, Zihao Dongfang, Haocong He, Yuanhuiyi Lyu, Lutao Jiang, Lu Qi, Li Chen, Danda Pani Paudel, Kailun Yang, Linfeng Zhang, Luc Van Gool, Xuming Hu

arXiv:2509.12989v1cs.CV

TL;DR

Embodied tasks such as navigation call for holistic 360° perception, while panoramic research faces data and model gaps. The paper surveys recent advances and proposes PANORAMA, an integrated architecture and roadmap for omnidirectional vision. It identifies cross-community practical applications alongside unresolved challenges.

  • Problem

    Panoramic data bottlenecks and limitations in existing models constrain omnidirectional vision for embodied tasks that require holistic environmental perception.

    Source: Motivation & Problem Space · p. 1

  • Method

    The paper synthesizes advances in omnidirectional vision and proposes PANORAMA, a four-subsystem architecture, with a staged roadmap toward a unified model.

    Source: PANORAMA System Architecture · p. 3, Emerging Trends & Future Roadmap · p. 4

  • Results

    The overview presents PANORAMA as an integrated architecture with four subsystems for connecting panoramic vision and embodied AI.

    Source: PANORAMA System Architecture · p. 3

  • Takeaways & Limitations

    The paper calls for application engineers to demonstrate omnidirectional perception in real-world robotics and interactive systems to bridge laboratory research and practical applications.

    Source: Challenges · p. 6

    Current approaches address static panoramic distortion as a frame-independent problem, while real-world distortion is dynamic and calls for temporal-consistency research.

    Source: Challenges · p. 6

Abstract

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Omnidirectional vision, using 360-degree vision to understand the environment, has become increasingly critical across domains like robotics, industrial inspection, and environmental monitoring. Compared to traditional pinhole vision, omnidirectional vision provides holistic environmental awareness, significantly enhancing the completeness of scene perception and the reliability of decision-making. However, foundational research in this area has historically lagged behind traditional pinhole vision. This talk presents an emerging trend in the embodied AI era: the rapid development of omnidirectional vision, driven by growing industrial demand and academic interest. We highlight recent breakthroughs in omnidirectional generation, omnidirectional perception, omnidirectional understanding, and related datasets. Drawing on insights from both academia and industry, we propose an ideal panoramic system architecture in the embodied AI era, PANORAMA, which consists of four key subsystems. Moreover, we offer in-depth opinions related to emerging trends and cross-community impacts at the intersection of panoramic vision and embodied AI, along with the future roadmap and open challenges. This overview synthesizes state-of-the-art advancements and outlines challenges and opportunities for future research in building robust, general-purpose omnidirectional AI systems in the embodied AI era.

Motivation & Problem Space

Embodied tasks such as navigation call for holistic 360° surroundings, beyond the frustum-limited view of pinhole vision. Omnidirectional vision faces linked data, model, and application gaps that constrain its development for embodied AI.

Data Bottlenecks

Model Capabilities

Application Blanks

  • Scenario-specific areas such as panoramic production-safety inspection and forest-fire detection remain underexplored amid limited interdisciplinary expertise and panoramic data and models [Yan et al. 2024; Jung et al. 2025; Fu, Lou, and Yu 2025].The paper groups these gaps with data bottlenecks and model capabilities as challenges to address systematically.Source: Motivation & Problem Space · p. 2

Recent Technical Advances

The overview organizes recent omnidirectional-vision work around generation, perception, and understanding, alongside the datasets supporting these tasks. It describes emerging methods and resources while noting limits in multimodal data and reasoning benchmarks.

Recent Technical Advances

  • Recent omnidirectional research spans generation, perception, and understanding, with generation pursuing structurally consistent panoramas and understanding emphasizing spatial interpretation.Perception adapts models for pinhole tasks to omnidirectional inputs.Source: Recent Technical Advances · p. 2

Omnidirectional Generation

  • GAN-based methods preceded diffusion approaches in panorama generation; Dream360 uses viewport-based outpainting and frequency-aware refinement, while PanoDiffusion incorporates RGB-D training inputs [Ai et al. 2024; Wu, Zheng, and Cham 2023].OmniDrag controls panorama generation using trajectories [Li et al. 2024c].Source: Omnidirectional Generation · p. 2

Omnidirectional Perception

Omnidirectional Understanding

Dataset

  • The overview identifies 23 representative datasets across indoor, outdoor, and UAV/flight domains, categorized by real or synthetic source and compared by scale, modalities, and annotations.Resources vary in annotation granularity and coverage; fully synchronized panoramic RGB, LiDAR, spatial audio, and behavioral-trace collections remain rare, and reasoning benchmarks are nascent [Kim et al. 2024; Dongfang et al. 2025; Zhang, Ye, and Zheng 2025].Source: Dataset · p. 3

PANORAMA System Architecture

PANORAMA is an ideal architecture for integrating panoramic vision with embodied AI, organized as four connected subsystems. Its workflow links sensor inputs through perception and applications, with acceleration supporting edge deployment.

PANORAMA System Architecture

  • PANORAMA proposes four integrated subsystems to connect panoramic vision with embodied AI.The architecture is presented as a potential solution to integration challenges.Source: PANORAMA System Architecture · p. 3

Subsystem 1: Data Acquisition & Pre-processing

  • Data acquisition captures raw omnidirectional images and videos, converts formats such as ERP and cubemap, and synchronizes and calibrates camera and complementary sensor inputs.The subsystem includes panoramic cameras and sensors such as IMUs and depth sensors.Source: Subsystem 1: Data Acquisition &… · pp. 3–4

Subsystem 2: Perception

  • Perception uses models adapted to spherical geometry, including spherical CNNs and transformers, for feature extraction and tasks such as segmentation, detection, and depth estimation.The listed tasks can share a feature backbone.Source: Subsystem 2: Perception · p. 4

Subsystem 3: Application

  • The application subsystem consumes structured perceptual outputs for embodied tasks including navigation and SLAM, human-robot interaction, and digital-twin reconstruction.Examples include semantic maps, object lists, and depth information as inputs.Source: Subsystem 3: Application · p. 4

Subsystem 4: Acceleration & Employment

  • Acceleration addresses high-resolution processing under resource constraints through software optimization and edge hardware deployment.Quantization and pruning balance accuracy, latency, and power consumption; cited hardware examples include NVIDIA Jetson and SOPHGO SE9.Source: Subsystem 4: Acceleration & Employment · p. 4

Workflow

  • The workflow passes corrected, synchronized sensor data to perception, then uses its outputs in embodied applications, while acceleration supports low-latency edge operation.The described pipeline runs from raw sensor inputs to embodied applications.Source: Workflow · p. 4

Emerging Trends & Future Roadmap

The roadmap responds to task-specific methods, projection ambiguities, and limited multimodal pretraining resources with six stages toward a unified omnidirectional model. It moves from dataset and sensor resources through training and evaluation to deployment and generalization.

Emerging Trends & Future Roadmap

  • Existing approaches show progress in perception, reasoning, navigation, and grasping, but many remain task-specific, face projection ambiguities, and lack large-scale multimodal pretraining resources.The paper proposes a six-stage roadmap toward an ideal unified omnidirectional model.Source: Emerging Trends & Future Roadmap · p. 4

Stage 1: Dataset Integration

  • Stage 1 integrates datasets across projections, standardizes splits and labels, and supplies reprojection tools for fair model comparisons.The planned benchmark suite includes human checks to improve annotation accuracy.Source: Stage 1: Dataset Integration · p. 5

Stage 2: Multi-Modal Expansion

  • Stage 2 synchronizes RGB, depth, LiDAR, audio, and IMU for multimodal, multitask pretraining and plans a public multisensor corpus.The plan uses real-synthetic pipelines to offset large-scale data-collection costs.Source: Stage 2: Multi-Modal Expansion · p. 5

Stage 3: Reasoning and Embodied Data

  • Stage 3 develops reasoning data and benchmarks for grounded VQA, instruction-following, navigation, and grasping, using hybrid question generation and simulation.Planned protocols measure model success rates in real-world interactive environments.Source: Stage 3: Reasoning and Embodied Data · p. 5

Stage 4: Unified Model Pretraining

  • Stage 4 fine-tunes and post-trains unified multitask encoders on panoramic-specific data, combining cross-projection representations, multi-objective losses, and real-synthetic curricula.The models jointly process 360° geometry, semantic labels, and synchronized sensor streams to support transferability.Source: Stage 4: Unified Model Pretraining · p. 5

Stage 5: Evaluation and Benchmarking

  • Stage 5 establishes standardized splits, reprojection tools, and harmonized metrics for accuracy, cross-projection consistency, reasoning, and embodiment.Evaluation also includes out-of-distribution splits, calibration, uncertainty, efficiency targets, and human verification for critical tasks.Source: Stage 5: Evaluation and Benchmarking · p. 5

Stage 6: Deployment and Generalization

  • Stage 6 tests real-world deployment and robustness through cross-domain transfer, continual learning, and out-of-distribution evaluation.The plan includes a deployment kit with stress-test data, adaptation benchmarks, and uncertainty-validation workflows.Source: Stage 6: Deployment and Generalization · pp. 5–6

Challenges

Omnidirectional vision is framed as an enabling technology with impacts beyond a single community, while several open challenges remain. The proposed directions span broader data and model development, real-world applications, and more robust, action-oriented systems.

Cross-community impacts

  • Omnidirectional vision is presented as a cross-community enabling technology, with applications spanning navigation, human-robot interaction, and cognitive agents.The discussion connects panoramic perception to situational awareness, social and spatial understanding, and information-rich inputs for cognitive abilities.Source: Challenges · p. 6

Generalization and Robustness

  • Current models often target specific scenarios or projection methods, leaving generalization across sensors, applications, and projections an open challenge.Proposed directions include projection-agnostic representations and self-supervised learning from unlabeled omnidirectional images and video.Source: Challenges · p. 6

Dynamic Distortion Handling

  • Current approaches address panoramic distortion as a frame-independent geometric problem, while real-world distortion is dynamic across video sequences.Future research should consider temporal consistency and distortion evolution.Source: Challenges · p. 6

Action-Aware Representation Learning

  • A key direction is action-aware representation learning that integrates omnidirectional visual features into downstream control strategies for robotic behavior.The stated goal extends beyond better observation to more effective action and decision-making.Source: Challenges · p. 6

Scalable and Unified Architectures

  • Unified multi-task foundation models for omnidirectional vision remain a challenge beyond inefficient task-specific models.The proposed models would be pretrained on extensive panoramic data to capture geometry and semantics, then specialized for applications.Source: Challenges · p. 6

Research directions

  • The discussion calls for large-scale multi-task datasets, novel omnidirectional architectures, and real-world demonstrations in robotics and interactive systems.These directions are assigned respectively to dataset creators, algorithm researchers, and application engineers.Source: Challenges · p. 6
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