What Is Argus 1.0
Argus 1.0 is the world's first spatial large model that supports panoramic image input, released by Realsee. Trained on nearly one million sets of real high-definition spatial data accumulated by Realsee, it adopts the Transformer architecture and features millisecond-level real-time reconstruction capability. It can rapidly infer camera pose, depth maps, and point maps from panoramic images or ordinary photos, generating high-precision 3D spatial models. Argus 1.0 has strong multi-source adaptability, is compatible with a variety of image inputs, and generates 3D spaces of high quality, significantly outperforming other similar solutions.
Main Features of Argus 1.0
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Panoramic Image Input and Real-Time Reconstruction: Supports panoramic image and ordinary photo input, can reconstruct 3D space within milliseconds, generates high-precision depth maps and point maps, and provides real-time global reconstruction capability.
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Multi-Source Adaptability: Compatible with a variety of image sources, including single or multiple ordinary photos, AI-generated images, etc., adapting to data input needs in different scenarios.
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High-Precision Spatial Generation: Based on large-scale real data training, the generated 3D spatial models are of high quality, can accurately restore spatial layouts and details, and are suitable for complex environments such as glass and mirrors.
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Multi-Domain Applications: Widely used in fields such as real estate rental and sales, home decoration and furnishings, commercial retail, and industrial parks, providing functions such as immersive experiences, design assistance, and digital spatial replication.
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Data-Driven Continuous Optimization: Relying on the world's largest 3D spatial database accumulated by Realsee, Argus 1.0 can continuously iterate to improve model performance and generation quality.
Technical Principles of Argus 1.0
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First-Ever Panoramic Image Input: Argus 1.0 is the first depth inference large model that supports panoramic image input, is compatible with single or multiple ordinary photos as well as AI-generated images, and has strong multi-source adaptability.
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Transformer Architecture: The model is built on the Transformer architecture, leveraging its powerful parallel processing capability and ability to capture long-range dependencies to improve model performance and generation quality and achieve high-precision spatial reconstruction.
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Large-Scale Real Data Training: It is trained on nearly one million sets of real high-definition spatial data accumulated by Realsee. These data cover a variety of scenarios and complex environments, ensuring that the 3D spaces generated by the model are highly precise and reliable.
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Millisecond-Level Real-Time Reconstruction: Through optimized algorithms and architecture design, Argus 1.0 achieves millisecond-level real-time reconstruction capability, can respond quickly to input, and provides an imperceptible user experience.
Argus 1.0 Project Address
- Project Official Website：https://h5.realsee.cn/argus
Application Scenarios of Argus 1.0
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Real Estate Rental and Sales: Can be used to generate virtual property viewing scenes, provide a more immersive viewing experience, and help users understand the spatial layout and details of a property more intuitively.
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Home Decoration and Furnishings: Can provide precise spatial models for home decoration design. Designers can create renovation plans that are more closely aligned with reality based on the models, and users can also preview the renovation results more clearly.
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Commercial Retail: Can digitally replicate commercial spaces for online display and marketing. Consumers can remotely browse the layouts and product displays of shopping malls and stores.
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Industrial Parks: Model factory buildings and facilities in industrial parks to facilitate management and planning, and it can also be used for remote inspection and maintenance.
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Dynamic lock screen spatial wallpapers: Argus 1.0 can be used to generate dynamic lock screen spatial wallpapers, with higher depth estimation accuracy and resolution, outperforming the depth estimation algorithm disclosed by Apple.