ARXIV:2603.17995 · 3D SHAPE GENERATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

LoST: Level of Semantics Tokenization for 3D Shapes

Niladri Shekhar Dutt · Zifan Shi · Paul Guerrero · Chun-Hao Paul Huang · Duygu Ceylan · Niloy J. Mitra · +1 at arXiv

LoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain LoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

LoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency. In particular, it plays a critical role in autoregressive (AR) models, which have recently emerged as a compelling…

METHOD

Full abstract

Tokenization is a fundamental technique in the generative modeling of various modalities. In particular, it plays a critical role in autoregressive (AR) models, which have recently emerged as a compelling option for 3D generation. However, optimal tokenization of 3D shapes remains an open question. State-of-the-art (SOTA) methods primarily rely on geometric level-of-detail (LoD) hierarchies, originally designed for rendering and compression. These spatial hierarchies are often token-inefficient and lack semantic coherence for AR modeling. We propose Level-of-Semantics Tokenization (LoST), which orders tokens by semantic salience, such that early prefixes decode into complete, plausible shapes that possess principal semantics, while subsequent tokens refine instance-specific geometric and semantic details. To train LoST, we introduce Relational Inter-Distance Alignment (RIDA), a novel 3D semantic alignment loss that aligns the relational structure of the 3D shape latent space with that of the semantic DINO feature space. Experiments show that LoST achieves SOTA reconstruction, surpassing previous LoD-based 3D shape tokenizers by large margins on both geometric and semantic reconstruction metrics. Moreover, LoST achieves efficient, high-quality AR 3D generation and enables downstream tasks like semantic retrieval, while using only 0.1%-10% of the tokens needed by prior AR models.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments show that LoST achieves SOTA reconstruction, surpassing previous LoD-based 3D shape tokenizers by large margins on both geometric and semantic reconstruction metrics.

WHY NOW

3D Shape Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainLoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

LoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

ARXIV:2603.17995 · 3D SHAPE GENERATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

LoST: Level of Semantics Tokenization for 3D Shapes

Niladri Shekhar Dutt · Zifan Shi · Paul Guerrero · Chun-Hao Paul Huang · Duygu Ceylan · Niloy J. Mitra · +1 at arXiv

LoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain LoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

3D Shape Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainLoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

LoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.17995

LoST: Level of Semantics Tokenization for 3D Shapes

LoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback38 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

3D Shape Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
LoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency. In particular, it plays a critical role in autoregressive (AR) models, which have recently emerged as a compelling option for 3D generation.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Tokenization is a fundamental technique in the generative modeling of various modalities. In particular, it plays a critical role in autoregressive (AR) models, which have recently emerged as a compelling option for 3D generation.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments show that LoST achieves SOTA reconstruction, surpassing previous LoD-based 3D shape tokenizers by large margins on both geometric and semantic reconstruction metrics.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
3D Shape Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

3D Shape Generation

Competitors

not indexed

Competitive landscape

LoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency.

Segment

3D Shape Generation

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.17995 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(38)

VertexRegen: Mesh Generation with Continuous Level of Detail

2025Xiang Zhang, Yawar Siddiqui et al.

ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding

2025Junliang Ye, Zhengyi Wang et al.

Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets

2025Weiyu Li, Xuanyang Zhang et al.

OctGPT: Octree-based Multiscale Autoregressive Models for 3D Shape Generation

2025Si-Tong Wei, Rui-Huan Wang et al.

"Principal Components" Enable A New Language of Images

2025Xin Wen, Bingchen Zhao et al.

FlexTok: Resampling Images into 1D Token Sequences of Flexible Length

2025Roman Bachmann, Jesse Allardice et al.

Structured 3D Latents for Scalable and Versatile 3D Generation

2024Jianfeng Xiang, Zelong Lv et al.

LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models

2024Zhengyi Wang, Jonathan Lorraine et al.

Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

2024Sihyun Yu, Sangkyung Kwak et al.

Autoregressive Image Generation without Vector Quantization

2024Tianhong Li, Yonglong Tian et al.

An Image is Worth 32 Tokens for Reconstruction and Generation

2024Qihang Yu, Mark Weber et al.

Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

2024Peize Sun, Yi Jiang et al.

MeshXL: Neural Coordinate Field for Generative 3D Foundation Models

2024Sijin Chen, Xin Chen et al.

Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer

2024Shuang Wu, Youtian Lin et al.

MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers

2023Yawar Siddiqui, A. Alliegro et al.

Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

2023Lijun Yu, José Lezama et al.

Vision Transformers Need Registers

2023Timothée Darcet, Maxime Oquab et al.

3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models

2023Biao Zhang, Jiapeng Tang et al.

Scalable Diffusion Models with Transformers

2022William S. Peebles, Saining Xie

Objaverse: A Universe of Annotated 3D Objects

2022Matt Deitke, Dustin Schwenk et al.

Showing 20 of 38 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkSemantic One-Dimensional Tokenizer for Image Reconstruction and Generation

7.0

Prior WorkLoSATok: Low-dimensional Semantic-Acoustic Tokenizer for Cross-Domain Audio Understanding and Generation

7.0

Prior WorkToLL: Topological Layout Learning with Structural Multi-view Augmentation for 3D Scene Graph Pretraining

7.0

Prior Work<SOG_k>: One LLM Token for Explicit Graph Structural Understanding

7.0

Prior WorkSSR3D-LLM: Structured Spatial Reasoning via Latent Steps for Fine-Grained Grounding in Unified 3D-LLMs

7.0

Prior Work(1D) Ordered Tokens Enable Efficient Test-Time Search

7.0

Extension

Builds On ThisLearning Sparse Visual Representations via Spatial-Semantic Factorization

5.0

Builds On ThisDo We Need Distinct Representations for Every Speech Token? Unveiling and Exploiting Redundancy in Large Speech Language Models

4.0

Builds On ThisSemantic-Aware Prefix Learning for Token-Efficient Image Generation

4.0

Builds On ThisToward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning

6.0

Commercially relevant

none indexed

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.17995
Route: /paper/lost-level-of-semantics-tokenization-for-3d-shapes
Active tab: read
Artifact: lost-level-of-semantics-tokenization-for-3d-shapes

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/lost-level-of-semantics-tokenization-for-3d-shapes/paper-pack
REST build passport API/api/v1/paper/lost-level-of-semantics-tokenization-for-3d-shapes/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
  "contract_version": "paper-r2",
  "paper_id": "85dc9e08-6dda-4b74-aa8b-673474d9c4c7",
  "arxiv_id": "2603.17995",
  "canonical_route": "/paper/lost-level-of-semantics-tokenization-for-3d-shapes",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "lost-level-of-semantics-tokenization-for-3d-shapes",
  "endpoints": {
    "paper_pack": "/api/v1/paper/lost-level-of-semantics-tokenization-for-3d-shapes/paper-pack",
    "build_passport": "/api/v1/paper/lost-level-of-semantics-tokenization-for-3d-shapes/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/lost-level-of-semantics-tokenization-for-3d-shapes

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

LoST: Level of Semantics Tokenization for 3D Shapes

Canonical ID lost-level-of-semantics-tokenization-for-3d-shapes | Route /paper/lost-level-of-semantics-tokenization-for-3d-shapes

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/lost-level-of-semantics-tokenization-for-3d-shapes

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.17995"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "LoST: Level of Semantics Tokenization for 3D Shapes",
  "normalized_query": "2603.17995",
  "route": "/paper/lost-level-of-semantics-tokenization-for-3d-shapes",
  "paper_ref": "lost-level-of-semantics-tokenization-for-3d-shapes",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: LoST: Level of Semantics Tokenization for 3D Shapes

/buildability/lost-level-of-semantics-tokenization-for-3d-shapes

Watchwatch

Subject: LoST: Level of Semantics Tokenization for 3D Shapes

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/lost-level-of-semantics-tokenization-for-3d-shapes

Paper ref

lost-level-of-semantics-tokenization-for-3d-shapes

arXiv id

2603.17995

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

2e767c28bee879fd7084ddd29e19dccf1f8f312915a88023e9f6b9a2dc5ed77b

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "WebPage",
      "@id": "https://sciencetostartup.com/paper/lost-level-of-semantics-tokenization-for-3d-shapes#webpage",
      "url": "https://sciencetostartup.com/paper/lost-level-of-semantics-tokenization-for-3d-shapes",
      "name": "LoST: Level of Semantics Tokenization for 3D Shapes",
      "description": "LoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency.",
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      }
    },
    {
      "@type": "ScholarlyArticle",
      "@id": "https://sciencetostartup.com/paper/lost-level-of-semantics-tokenization-for-3d-shapes#scholarlyArticle",
      "headline": "LoST: Level of Semantics Tokenization for 3D Shapes",
      "description": "LoST revolutionizes 3D shape generation by introducing a semantic tokenization method that enhances autoregressive modeling efficiency.",
      "url": "https://sciencetostartup.com/paper/lost-level-of-semantics-tokenization-for-3d-shapes",
      "sameAs": "https://arxiv.org/abs/2603.17995",
      "identifier": {
        "@type": "PropertyValue",
        "propertyID": "arXiv",
        "value": "2603.17995"
      },
      "isAccessibleForFree": true,
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      },
      "datePublished": "2026-03-18T17:56:06.000Z",
      "author": [
        {
          "@type": "Person",
          "name": "Niladri Shekhar Dutt"
        },
        {
          "@type": "Person",
          "name": "Zifan Shi"
        },
        {
          "@type": "Person",
          "name": "Paul Guerrero"
        },
        {
          "@type": "Person",
          "name": "Chun-Hao Paul Huang"
        },
        {
          "@type": "Person",
          "name": "Duygu Ceylan"
        },
        {
          "@type": "Person",
          "name": "Niloy J. Mitra"
        },
        {
          "@type": "Person",
          "name": "Xuelin Chen"
        }
      ],
      "citation": [
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "45d5d4bf72edeca0c9110b0ce7e3c565b437343a"
          },
          "url": "https://www.semanticscholar.org/paper/45d5d4bf72edeca0c9110b0ce7e3c565b437343a"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "2370c14e3090893a935f8bddd45f8cdef0265dc9"
          },
          "url": "https://www.semanticscholar.org/paper/2370c14e3090893a935f8bddd45f8cdef0265dc9"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "6da84f798de781b6dfa6308abffa00a5168099bc"
          },
          "url": "https://www.semanticscholar.org/paper/6da84f798de781b6dfa6308abffa00a5168099bc"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "f680fb0f8873e4c0ee0a28763404d192d370743f"
          },
          "url": "https://www.semanticscholar.org/paper/f680fb0f8873e4c0ee0a28763404d192d370743f"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a5c1a7d76a957ab750e0eb708966d18a7385baa1"
          },
          "url": "https://www.semanticscholar.org/paper/a5c1a7d76a957ab750e0eb708966d18a7385baa1"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a3ebbaabc51e6f666802bcbccc149027bdc9f5a9"
          },
          "url": "https://www.semanticscholar.org/paper/a3ebbaabc51e6f666802bcbccc149027bdc9f5a9"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "5666d551bc9f86e2f379ede8b8ffddbe4d1d53a8"
          },
          "url": "https://www.semanticscholar.org/paper/5666d551bc9f86e2f379ede8b8ffddbe4d1d53a8"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "b9806875c6472434f99ac1e50055d240c3bfb3b6"
          },
          "url": "https://www.semanticscholar.org/paper/b9806875c6472434f99ac1e50055d240c3bfb3b6"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "f5ac95fa58815a35622a60f42a5729f518e57cdf"
          },
          "url": "https://www.semanticscholar.org/paper/f5ac95fa58815a35622a60f42a5729f518e57cdf"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "e46a79cf7c2c9192639f9e1d12280650e06b9d56"
          },
          "url": "https://www.semanticscholar.org/paper/e46a79cf7c2c9192639f9e1d12280650e06b9d56"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "1e31f4a4ccfc0d1e461be05361d77b5e045f4d37"
          },
          "url": "https://www.semanticscholar.org/paper/1e31f4a4ccfc0d1e461be05361d77b5e045f4d37"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "b15e6e2b1d81bc110f8fc98c3caf2e25e2512539"
          },
          "url": "https://www.semanticscholar.org/paper/b15e6e2b1d81bc110f8fc98c3caf2e25e2512539"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "f0d37c2af188ec448eaa0ffd729b8bc8724dc17a"
          },
          "url": "https://www.semanticscholar.org/paper/f0d37c2af188ec448eaa0ffd729b8bc8724dc17a"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "f3a2e9a43ef9af111d0ccf74d3efa08607f00575"
          },
          "url": "https://www.semanticscholar.org/paper/f3a2e9a43ef9af111d0ccf74d3efa08607f00575"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "e44a11954715bb36df0303e25783968e5f3bd610"
          },
          "url": "https://www.semanticscholar.org/paper/e44a11954715bb36df0303e25783968e5f3bd610"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "985f0c89c5a607742ec43c1fdc2cbfe54541cbad"
          },
          "url": "https://www.semanticscholar.org/paper/985f0c89c5a607742ec43c1fdc2cbfe54541cbad"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "10bd38673951f5d7729568284093cbd80482ab16"
          },
          "url": "https://www.semanticscholar.org/paper/10bd38673951f5d7729568284093cbd80482ab16"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "eb35863662544c977780299c21e669555ae83e81"
          },
          "url": "https://www.semanticscholar.org/paper/eb35863662544c977780299c21e669555ae83e81"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "736973165f98105fec3729b7db414ae4d80fcbeb"
          },
          "url": "https://www.semanticscholar.org/paper/736973165f98105fec3729b7db414ae4d80fcbeb"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "1b31dbf44e68b698120552366df03e6e35a1e428"
          },
          "url": "https://www.semanticscholar.org/paper/1b31dbf44e68b698120552366df03e6e35a1e428"
        }
      ],
      "additionalProperty": [
        {
          "@type": "PropertyValue",
          "propertyID": "viabilityScore",
          "value": 7
        },
        {
          "@type": "PropertyValue",
          "propertyID": "researchDomain",
          "value": "3D Shape Generation"
        }
      ]
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "position": 1,
          "name": "Home",
          "item": "https://sciencetostartup.com"
        },
        {
          "@type": "ListItem",
          "position": 2,
          "name": "3D Shape Generation",
          "item": "https://sciencetostartup.com/topics"
        },
        {
          "@type": "ListItem",
          "position": 3,
          "name": "LoST: Level of Semantics Tokenization for 3D Shapes",
          "item": "https://sciencetostartup.com/paper/lost-level-of-semantics-tokenization-for-3d-shapes"
        }
      ]
    }
  ]
}

LoST: Level of Semantics Tokenization for 3D Shapes

LoST: Level of Semantics Tokenization for 3D Shapes

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(38)

Related Papers

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(38)

Related Papers

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline