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arXiv · 2506.14471

Dense360: Dense Understanding from Omnidirectional Panoramas

Yikang Zhou·Tao Zhang·Dizhe Zhang·Shunping Ji·Xiangtai Li·Lu Qi
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Abstract

Multimodal Large Language Models (MLLMs) require comprehensive visual inputs to achieve dense understanding of the physical world. While existing MLLMs demonstrate impressive world understanding capabilities through limited field-of-view (FOV) visual inputs (e.g., 70 degree), we take the first step toward dense understanding from omnidirectional panoramas. We first introduce an omnidirectional panoramas dataset featuring a comprehensive suite of reliability-scored annotations. Specifically, our dataset contains 160K panoramas with 5M dense entity-level captions, 1M unique referring expressions, and 100K entity-grounded panoramic scene descriptions. Compared to multi-view alternatives, panoramas can provide more complete, compact, and continuous scene representations through equirectangular projections (ERP). However, the use of ERP introduces two key challenges for MLLMs: i) spatial continuity along the circle of latitude, and ii) latitude-dependent variation in information density. We address these challenges through ERP-RoPE, a position encoding scheme specifically designed for panoramic ERP. In addition, we introduce Dense360-Bench, the first benchmark for evaluating MLLMs on omnidirectional captioning and grounding, establishing a comprehensive framework for advancing dense visual-language understanding in panoramic settings.

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Yikang Zhou, Tao Zhang, Dizhe Zhang, Shunping Ji, Xiangtai Li, Lu Qi. 2025-06-17. Dense360: Dense Understanding from Omnidirectional Panoramas. https://arxiv.org/abs/2506.14471

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