Multi-Modal Panoramic Representation

Updated 27 December 2025

Multi-modal panoramic representation is a unified modeling approach that maps sensors like RGB, depth, LiDAR, and more onto a 360° field using specialized panoramic projections.
It employs precise geometric calibration and cross-modal alignment techniques to ensure consistent fusion of diverse sensor data despite distortions.
Advanced transformer-based architectures and bespoke loss functions drive robust performance in scene understanding, immersive generation, and robotic navigation applications.

A multi-modal panoramic representation is a unified modeling approach in which multiple sensor modalities—such as RGB, depth, LiDAR, reflectivity, ambient near-infrared, audio, and semantic maps—are jointly structured over a 360° field of view. Central to this paradigm is the use of panoramic projections (equirectangular, cubemap, or specialized hybrid topologies) allowing dense, geometrically and semantically consistent fusion across modalities for tasks such as scene understanding, navigation, generation, and sensor translation. Research in this area integrates advances in geometry-aware data alignment, high-dimensional multi-modal fusion, panoramic vision transformers, and generative modeling, supporting applications from robotic mapping to immersive scene synthesis and visual grounding.

1. Sensor Modalities and Panoramic Data Structures

Multi-modal panoramic systems are defined by the tightly coupled acquisition and mapping of multiple sensor channels onto a common panoramic domain. DurLAR (Li et al., 14 Jun 2024) typifies high-fidelity mobile autonomous driving data with:

128-channel 3D LiDAR: 128 vertical channels (±22.5° pitch, 2048 columns at 10 Hz), recording per-point xyz and intensity, with range accuracy ±2 cm at 20–50 m and up to 120 m range.
Panoramic Ambient (NIR) Imagery: 2048×128 pixels, monochrome, capturing ambient radiance within 800–2,500 nm for robust low-light imaging.
Reflectivity Panoramas: Computed as $I\cdot r^2$ (Lambertian inverse-square law) and mapped identically to the LiDAR, supporting material/texture-aware representation.

Other pipelines extend panoramic representation through:

RGB/depth/semantic modalities: Equirectangular or cubemap mapping of reconstructed or rendered RGB-D and semantic channels, as in PanoGrounder (Jung et al., 24 Dec 2025).
Audio and environmental context: Multi-microphone audio, binaural delay, and GPS, integrated into temporally synchronized 360° video streams (Chen et al., 1 Apr 2024).
Feature/language/geometry embeddings: ViT-extracted semantic and geometric tokens reprojected into panoramic images for VLM-based tasks (Jung et al., 24 Dec 2025, Zhou et al., 17 Jun 2025).

Common to all is rigorous coordinate alignment—either explicit calibration (intrinsics/extrinsics), algorithmic viewpoint selection, or geometric sampling—ensuring spatial congruence and temporally synchronized data acquisition.

Precise registration and transformation across modalities is essential:

Intrinsic/Extrinsic Calibration: Camera intrinsics (focal lengths, principal point) and multi-sensor extrinsics (e.g., LiDAR–camera, IMU–camera) are estimated via marker-based pose recovery and RANSAC-based edge alignment, as in DurLAR (Li et al., 14 Jun 2024).
Spherical/Equirectangular Projections: All modalities are mapped using spherical coordinate transforms: azimuth $\theta$ and elevation $\phi$ are computed per column and row; 3D points are projected via known transformations into panoramic image indices.
Distortion and Augmentation: Panoramic fusion requires both global wrap-around continuity and local distortion handling, using learned deformable offsets (Guttikonda et al., 2023), or spherical geometry-constrained sampling (Zhang et al., 12 Mar 2025), to mitigate non-uniform projection effects and sampling density near poles/equator.
Synchronization: All sensor streams (LiDAR, stereo, GPS, audio) are precisely timestamped (often at 10–25 Hz) for frame-wise fusion.

This calibration infrastructure underpins end-to-end multi-modal learning, enabling geometry-aware fusion networks to operate on tightly registered panoramic inputs.

Contemporary multi-modal panoramic models unify sensory signals either through feature-level or output-level fusion:

Transformer-based Encoders: SegFormer/ViT-style backbones, modified for equirectangular/cubemap inputs, serve as multi-modal feature extractors (Guttikonda et al., 2023, Zhang et al., 12 Mar 2025). Distortion-aware patch embedding modules and LayerNorm over concatenated cubemap tokens ensure flexible mapping between modalities and panoramic topology (Feng et al., 7 Dec 2025).
Cross-Modal Attention and Fusion: Cross-modal feature rectification modules (CM-FRM) and fusion blocks (FFM, Joint-Face Adapters) accomplish modality-to-modality alignment, injecting global and long-range context in networks for semantic segmentation and generation (Guttikonda et al., 2023, Feng et al., 7 Dec 2025).
Hybrid Representations: Systems like OmniMap (Deng et al., 9 Sep 2025) hybridize dense geometric (TSDF voxels) and explicit Gaussian Splatting fields, maintaining stability for occupancy and semantics while allowing high-fidelity rendering and direct 3D-to-panorama mapping.
Task-Conditioned Blocks: Unified modulation (e.g., condition switching in JoPano (Feng et al., 7 Dec 2025) or panoramic/text-class fusion in ViewPoint (Fang et al., 30 Jun 2025)) allows a single model to support text-conditioned, view-conditioned, or multi-modal input-driven panorama generation.

These architectures are typically coupled with additional augmentation strategies (random flips, MixUp, spherical distortions) to maintain correspondence under large-scale panoramic deformation (Zhang et al., 12 Mar 2025).

Panoramic multi-modal models use bespoke objective compositions to leverage dense and sparse supervision:

Joint Supervised/Self-Supervised Losses: For depth estimation, DurLAR (Li et al., 14 Jun 2024) combines BerHu (reverse Huber) loss for pixels with ground truth and photometric reprojection, smoothness, and temporal consistency terms (as in ManyDepth [51]).
Adversarial and Segmentation Losses: Panoramic style transfer and modality translation (LiDAR-to-RGB-D panorama) are driven by WGAN-GP adversarial objectives, class-weighted cross-entropy with Lovász-Softmax for direct IoU optimization, and auxiliary losses for depth (inverse smoothness, SSIM) (Cortinhal et al., 2023, Cortinhal et al., 2021).
Masked Pretext Tasks: Multi-modal masked pre-training masks both RGB and depth with a shared patch schedule, forcing alignment in context prediction and enabling efficient transfer for depth completion (Yan et al., 2022).
Generic Multi-Modal Objectives: For semantic/scene understanding and VLM-based grounding, softmax cross-entropy over pixel/patch labels is combined with alignment objectives (feature-level, language–image contrast) (Jung et al., 24 Dec 2025, Zhou et al., 17 Jun 2025).

Benchmarking typically utilizes RMSE, Sq Rel, mIoU, FID, CLIP-Score, and specially designed seam metrics (e.g., Seam-SSIM, Seam-Sobel for cubemap tiling) (Feng et al., 7 Dec 2025).

5. Evaluation Metrics, Benchmark Datasets, and Empirical Findings

Robust evaluation of multi-modal panoramic systems necessitates modality-balanced datasets and comprehensive metrics:

Dataset or Benchmark	Modalities	Key Metrics	Notable Results
DurLAR (Li et al., 14 Jun 2024)	LiDAR, Ambient,	Depth RMSE, SqRel, AbsRel	RMSE=3.639 m, Sq Rel=0.936 on monocular depth
	Reflectivity, RGB		estimation (with joint loss)
Structured3D/Matterport3D/Stanford2D3DS (Guttikonda et al., 2023)	RGB, Depth, Normals, HHA	mIoU semantic segmentation	Up to 71.97% mIoU with tri-modal fusion
360+x (Chen et al., 1 Apr 2024)	Video, Audio,	AP, mAP, Recall,	+7.5–10% AP from adding audio/ITD,
	Directional ITD	mIoU, contrastive Recall	80.6% AP (V+A+D, all views, classification)
JoPano (Feng et al., 7 Dec 2025)	Cube RGB	FID, CLIP-FID, IS, SSIM	FID=13.07 (V2P), Seam-SSIM, Seam-Sobel metrics
PanoGrounder (Jung et al., 24 Dec 2025)	RGB, reprojected geometry, semantics	[email protected] (3DVG)	74.6% (Nr3D), 61.0% (ScanRefer); +10% over pinhole

Benchmarks consistently show that multi-modal fusion improves performance on dense understanding tasks (e.g., 16.4 points IoU gain from ERP-aware positional encoding (Zhou et al., 17 Jun 2025)), object localization, grounding, and video scene classification; increased context and geometric completeness are direct consequences of panoramic modeling.

6. Representative Applications and Extension Pathways

Multi-modal panoramic representations underpin a broad range of high-impact applications:

Autonomous Systems: Robust 360° depth estimation, semantic segmentation, and obstacle detection/recognition in dynamic urban and indoor environments (Li et al., 14 Jun 2024, Zhang et al., 12 Mar 2025, Deng et al., 9 Sep 2025).
Robotics and Navigation: Panoramic-LiDAR fusion for BEV mapping and real-time navigation, panoramic scene understanding for manipulation and scene-level Q&A (Zhang et al., 12 Mar 2025, Deng et al., 9 Sep 2025).
Immersive Generation: Text- and view-conditioned 360° panorama and video synthesis with diffusion, supporting VR world building, spatial video modeling, and synthetic data generation (Feng et al., 7 Dec 2025, Fang et al., 30 Jun 2025).
Cross-Modal Domain Translation: Sensor-agnostic translation (LiDAR-to-RGB, depth/semantics aware) for robustness in adverse conditions, domain adaptation, and fail-over perception (Cortinhal et al., 2023, Cortinhal et al., 2021).
Panoramic Visual Language: Dense entity description, visual grounding, and panoramic captioning within MLLM frameworks (Zhou et al., 17 Jun 2025, Jung et al., 24 Dec 2025).
Data-Efficient Pre-Training: Multi-modal masked pre-training and structure-aware camera placement for sample-efficient, transferable panoramic feature learning (Yan et al., 2022, Jung et al., 24 Dec 2025).

Extension directions include multi-modal augmentation (thermal, radar, event cameras), direct learning on equirectangular or manifold representations, continual adaptation for sensor drift, and advanced scene-level reasoning in open-vocabulary and language-conditioned settings.

7. Challenges, Limitations, and Open Problems

Despite the substantial advances, several open issues persist:

Panoramic Distortion: Non-uniform angular stretching and pole effects in equirectangular projections demand dedicated attention and positional encoding schemes (Guttikonda et al., 2023, Zhou et al., 17 Jun 2025).
Sensor Sparsity and Calibration: Handling extremely sparse, noisy, and asynchronous multi-modal sensor data remains a central challenge, particularly for large-scale, in-the-wild deployments (Zhang et al., 12 Mar 2025, Deng et al., 9 Sep 2025).
Seamless Fusion across Topologies: Tiling, cubemap, and custom ViewPoint parametrizations must address seam artifacts, spatial aliasing, and pose-dependent blending for truly continuous modeling (Feng et al., 7 Dec 2025, Fang et al., 30 Jun 2025).
Compute and Data Efficiency: Most pipelines rely on significant compute (multi-GPU training) and high-fidelity calibration; generalization under limited data or lightweight deployment scenarios is an ongoing research question (Li et al., 14 Jun 2024, Zhang et al., 12 Mar 2025).
Modality Adaptation and Scalability: Extending to more exotic or dynamic sensor configurations and ensuring robust cross-domain transfer and adaptation challenge both algorithmic and dataset scaling (Chen et al., 1 Apr 2024, Jung et al., 24 Dec 2025).

A plausible implication is that future progress will couple enhanced geometric modeling (e.g. learned spherical manifolds, dynamic sensor arrays) with flexible fusion architectures and task-conditioned, self-supervised pre-training that natively operate on panoramic, multi-modal inputs.