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Collisions, feature-memory cost and scene/device-dependent quality-speed tradeoffs remain.", "domain": "Efficient neural graphics representations" }, "sources": [ { "citation": "Instant Neural Graphics Primitives with a Multiresolution Hash Encoding", "url": "https://arxiv.org/abs/2201.05989v1" }, { "citation": "Müller et al.: multiresolution hash encoding for neural graphics — author/institutional project", "url": "https://nvlabs.github.io/instant-ngp/" } ], "statement": "# Müller et al.: multiresolution hash encoding for neural graphics\n\nPaired trainable multiresolution hash-grid features with small neural networks and fused GPU kernels to accelerate fitting and rendering of neural graphics primitives.\n\n## Scientific scope\n\nNeRF, grid encodings and hashing have predecessors; acquire this representation/implementation combination, not invention of neural radiance fields. Collisions, feature-memory cost and scene/device-dependent quality-speed tradeoffs remain.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nThomas Müller; Alex Evans; Christoph Schied; Alexander Keller\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://arxiv.org/abs/2201.05989v1)\n- [Müller et al.: multiresolution hash encoding for neural graphics — author/institutional project](https://nvlabs.github.io/instant-ngp/)\n", "summary": "Paired trainable multiresolution hash-grid features with small neural networks and fused GPU kernels to accelerate fitting and rendering of neural graphics primitives.", "title": "Müller et al.: multiresolution hash encoding for neural graphics" }, "record_id": "urn:uuid:d98dc99b-2330-403b-a57a-ee81350fe88b", "version": 1, "source_ref": { "record_id": "urn:uuid:d98dc99b-2330-403b-a57a-ee81350fe88b", "version": 1, "content_hash": "sha256:c8fd4f5bb7ab064bd96c62a4fd2d4ab4b597a7d957d072d39aef28e531a92120" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:3c5291053ba4a3780db62f82189b84594da2743b223a5f15c5803f2b4943bacf" }, "target": { "record_id": "urn:uuid:d98dc99b-2330-403b-a57a-ee81350fe88b", "version": 1, "content_hash": "sha256:c8fd4f5bb7ab064bd96c62a4fd2d4ab4b597a7d957d072d39aef28e531a92120" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "muller-et-al-instant-ngp-hash-2022", "entry_id": "muller-et-al-instant-ngp-hash-2022", "revision": 1, "title": "Müller et al.: multiresolution hash encoding for neural graphics", "context": "Efficient neural graphics representations", "track": "Human-designed research method or resource", "band": "A", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Paired trainable multiresolution hash-grid features with small neural networks and fused GPU kernels to accelerate fitting and rendering of neural graphics primitives.", "scope": "Paired trainable multiresolution hash-grid features with small neural networks and fused GPU kernels to accelerate fitting and rendering of neural graphics primitives. 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Collisions, feature-memory cost and scene/device-dependent quality-speed tradeoffs remain.", "references": [ { "title": "Instant Neural Graphics Primitives with a Multiresolution Hash Encoding", "url": "https://arxiv.org/abs/2201.05989v1", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." }, { "title": "Müller et al.: multiresolution hash encoding for neural graphics — author/institutional project", "url": "https://nvlabs.github.io/instant-ngp/", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." } ], "roles": [ { "who": "Source-listed authors and technical contributors", "credit": "SCIENCE", "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities." } ], "target": { "record_id": "urn:uuid:d98dc99b-2330-403b-a57a-ee81350fe88b", "version": 1, "content_hash": "sha256:c8fd4f5bb7ab064bd96c62a4fd2d4ab4b597a7d957d072d39aef28e531a92120" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "170000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }