LERF: Language Embedded Radiance Fields

• URL: http://arxiv.org/abs/2303.09553v1
• Date: Thu, 16 Mar 2023 17:59:20 GMT
• Title: LERF: Language Embedded Radiance Fields
• Authors: Justin Kerr, Chung Min Kim, Ken Goldberg, Angjoo Kanazawa, Matthew Tancik
• Abstract summary: Language Embedded Radiance Fields (LERFs) is a method for grounding language embeddings from off-the-shelf models like CLIP into NeRF. LERFs learns a dense, multi-scale language field inside NeRF by volume rendering CLIP embeddings along training rays. After optimization, LERF can extract 3D relevancy maps for a broad range of language prompts interactively in real-time.
• Score: 35.925752853115476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans describe the physical world using natural language to refer to specific 3D locations based on a vast range of properties: visual appearance, semantics, abstract associations, or actionable affordances. In this work we propose Language Embedded Radiance Fields (LERFs), a method for grounding language embeddings from off-the-shelf models like CLIP into NeRF, which enable these types of open-ended language queries in 3D. LERF learns a dense, multi-scale language field inside NeRF by volume rendering CLIP embeddings along training rays, supervising these embeddings across training views to provide multi-view consistency and smooth the underlying language field. After optimization, LERF can extract 3D relevancy maps for a broad range of language prompts interactively in real-time, which has potential use cases in robotics, understanding vision-language models, and interacting with 3D scenes. LERF enables pixel-aligned, zero-shot queries on the distilled 3D CLIP embeddings without relying on region proposals or masks, supporting long-tail open-vocabulary queries hierarchically across the volume. The project website can be found at https://lerf.io .

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