ARXIV:2603.16868 · 3D SCENE RECONSTRUCTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

MessyKitchens: Contact-rich object-level 3D scene reconstruction

Q: What products could be built from this research?

Now is the time because advancements in neural architectures and datasets like MessyKitchens address the gap in physically-plausible scene reconstruction, coinciding with growing demand in robotics and AR for real-world deployment in unstructured settings.

Q: What are the practical use cases?

A warehouse automation system that uses monocular cameras to reconstruct cluttered shelves in 3D, enabling robots to identify and pick items without collisions, improving efficiency in logistics.

Junaid Ahmed Ansari · Ran Ding · Fabio Pizzati · Ivan Laptev · arXiv

MessyKitchens offers a novel dataset and advanced methods for accurate 3D scene reconstruction in cluttered environments.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain MessyKitchens offers a novel dataset and advanced methods for accurate 3D scene reconstruction in cluttered environments.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

MessyKitchens offers a novel dataset and advanced methods for accurate 3D scene reconstruction in cluttered environments. Powered by the modern neural architectures and large-scale data, recent methods achieve high performance in depth estimation from…

METHOD

Full abstract

Monocular 3D scene reconstruction has recently seen significant progress. Powered by the modern neural architectures and large-scale data, recent methods achieve high performance in depth estimation from a single image. Meanwhile, reconstructing and decomposing common scenes into individual 3D objects remains a hard challenge due to the large variety of objects, frequent occlusions and complex object relations. Notably, beyond shape and pose estimation of individual objects, applications in robotics and animation require physically-plausible scene reconstruction where objects obey physical principles of non-penetration and realistic contacts. In this work we advance object-level scene reconstruction along two directions. First, we introduceMessyKitchens, a new dataset with real-world scenes featuring cluttered environments and providing high-fidelity object-level ground truth in terms of 3D object shapes, poses and accurate object contacts. Second, we build on the recent SAM 3D approach for single-object reconstruction and extend it with Multi-Object Decoder (MOD) for joint object-level scene reconstruction. To validate our contributions, we demonstrate MessyKitchens to significantly improve previous datasets in registration accuracy and inter-object penetration. We also compare our multi-object reconstruction approach on three datasets and demonstrate consistent and significant improvements of MOD over the state of the art. Our new benchmark, code and pre-trained models will become publicly available on our project website: https://messykitchens.github.io/.

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Powered by the modern neural architectures and large-scale data, recent methods achieve high performance in depth estimation from a single image.

WHY NOW

3D Scene Reconstruction moved forward this cycle; last verified April 2026. Public score 8.0/10.

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

Opportunity summary

Score8.0

PainMessyKitchens offers a novel dataset and advanced methods for accurate 3D scene reconstruction in cluttered environments.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

MessyKitchens offers a novel dataset and advanced methods for accurate 3D scene reconstruction in cluttered environments.

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

ARXIV:2603.16868 · 3D SCENE RECONSTRUCTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

MessyKitchens: Contact-rich object-level 3D scene reconstruction

Junaid Ahmed Ansari · Ran Ding · Fabio Pizzati · Ivan Laptev · arXiv

MessyKitchens offers a novel dataset and advanced methods for accurate 3D scene reconstruction in cluttered environments.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain MessyKitchens offers a novel dataset and advanced methods for accurate 3D scene reconstruction in cluttered environments.

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 Scene Reconstruction moved forward this cycle; last verified April 2026. Public score 8.0/10.

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

Opportunity summary

Score8.0

PainMessyKitchens offers a novel dataset and advanced methods for accurate 3D scene reconstruction in cluttered environments.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

MessyKitchens offers a novel dataset and advanced methods for accurate 3D scene reconstruction in cluttered environments.

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

Paper Pack

10.48550/arXiv.2603.16868

MessyKitchens: Contact-rich object-level 3D scene reconstruction

MessyKitchens offers a novel dataset and advanced methods for accurate 3D scene reconstruction in cluttered environments.

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 materialized8 extracted61 indexednot indexed

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

Viability

8.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

3D Scene Reconstruction moved forward this cycle; last verified April 2026. Public score 8.0/10.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
we introduce MessyKitchens, a new dataset with real-world scenes featuring cluttered environments and providing high-fidelity object-level ground truth in terms of 3D object shapes, poses and accurate object contacts
Implicationpartial
Explicitly stated in the abstract as a key contribution of the work
Verificationpartial
partial
Evidencepartial
We also compare our multi-object reconstruction approach on three datasets and demonstrate consistent and significant improvements of MOD over the state of the art
Implicationpartial
Directly stated in abstract with clear comparison to state of the art
Verificationpartial
partial
Evidencepartial
we demonstrate MessyKitchens to significantly improve previous datasets in registration accuracy and inter-object penetration
Implicationpartial
Explicitly stated in abstract as a validation of the dataset contribution
Verificationpartial
partial
Evidencepartial
we build on the recent SAM 3D approach for single-object reconstruction and extend it with Multi-Object Decoder (MOD) for joint object-level scene reconstruction
Implicationpartial
Directly stated in abstract as the technical approach
Verificationpartial
partial
Evidencepartial
applications in robotics and animation require physically-plausible scene reconstruction where objects obey physical principles of non-penetration and realistic contacts
Implicationpartial
Explicitly stated in abstract as motivation and addressed by the dataset and method
Verificationpartial
partial
Evidencepartial
reconstructing and decomposing common scenes into individual 3D objects remains a hard challenge due to the large variety of objects, frequent occlusions and complex object relations
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
Monocular 3D scene reconstruction has recently seen significant progress
Implicationpartial
Explicitly stated in abstract as the input modality
Verificationpartial
partial
Evidencepartial
In this work we advance object-level scene reconstruction along two directions
Implicationpartial
Explicitly stated in abstract as the structure of contributions
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 Scene Reconstruction

Competitors

not indexed

Competitive landscape

MessyKitchens offers a novel dataset and advanced methods for accurate 3D scene reconstruction in cluttered environments.

Segment

3D Scene Reconstruction

Adoption evidence

No public code link in the paper record yet

Commercial read

8.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.16868 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(61)

SAM 3D: 3Dfy Anything in Images

2025S. Team, Xingyu Chen et al.

SAM 3: Segment Anything with Concepts

2025Nicolas Carion, Laura Gustafson et al.

Versatile and Generalizable Manipulation via Goal-Conditioned Reinforcement Learning with Grounded Object Detection

2025Huiyi Wang, Fahim Shahriar et al.

AnimateAnyMesh: A Feed-Forward 4D Foundation Model for Text-Driven Universal Mesh Animation

2025Zijie Wu, Chaohui Yu et al.

PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers

2025Yuchen Lin, Chenguo Lin et al.

ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping

2025Shun Iwase, Muhammad Zubair Irshad et al.

GraspClutter6D: A Large-Scale Real-World Dataset for Robust Perception and Grasping in Cluttered Scenes

2025Seunghyeok Back, Joosoon Lee et al.

VGGT: Visual Geometry Grounded Transformer

2025Jianyuan Wang, Minghao Chen et al.

Gen3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control

2025Xuanchi Ren, Tianchang Shen et al.

MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generation

2024Zehuan Huang, Yuan-Chen Guo et al.

TARGO: Benchmarking Target-driven Object Grasping under Occlusions

2024Yan Xia, Ran Ding et al.

Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Tracking

2024Jiyao Zhang, Weiyao Huang et al.

CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets

2024Longwen Zhang, Ziyu Wang et al.

REPARO: Compositional 3D Assets Generation with Differentiable 3D Layout Alignment

2024Haonan Han, Rui Yang et al.

KITchen: A Real-World Benchmark and Dataset for 6D Object Pose Estimation in Kitchen Environments

2024Abdelrahman Younes, Tamim Asfour

ComboVerse: Compositional 3D Assets Creation Using Spatially-Aware Diffusion Guidance

2024Yongwei Chen, Tengfei Wang et al.

Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

2024Tianhe Ren, Shilong Liu et al.

Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

2024Lihe Yang, Bingyi Kang et al.

DiffCAD: Weakly-Supervised Probabilistic CAD Model Retrieval and Alignment from an RGB Image

2023Daoyi Gao, Dávid Rozenberszki et al.

Uni-3D: A Universal Model for Panoptic 3D Scene Reconstruction

2023Xiang Zhang, Zeyuan Chen et al.

Showing 20 of 61 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 WorkCoherent Human-Scene Reconstruction from Multi-Person Multi-View Video in a Single Pass

8.0

Extension

Builds On ThisHuman Interaction-Aware 3D Reconstruction from a Single Image

7.0

Builds On ThisSurgCUT3R: Surgical Scene-Aware Continuous Understanding of Temporal 3D Representation

7.0

Builds On ThisA 3D Reconstruction Benchmark for Asset Inspection

6.0

Builds On ThisDense Dynamic Scene Reconstruction and Camera Pose Estimation from Multi-View Videos

7.0

Builds On ThisKitchenTwin: Semantically and Geometrically Grounded 3D Kitchen Digital Twins

7.0

Builds On ThisSHOW3D: Capturing Scenes of 3D Hands and Objects in the Wild

7.0

Builds On ThisDRoPS: Dynamic 3D Reconstruction of Pre-Scanned Objects

7.0

Builds On ThisWhich Reconstruction Model Should a Robot Use? Routing Image-to-3D Models for Cost-Aware Robotic Manipulation

7.0

Commercially relevant

none indexed

Conflicting

Competing ApproachReconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations

7.0

Related Resources

Owned Distribution

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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: 2603.16868
Route: /paper/messykitchens-contact-rich-object-level-3d-scene-reconstruction
Active tab: read
Artifact: messykitchens-contact-rich-object-level-3d-scene-reconstruction

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/messykitchens-contact-rich-object-level-3d-scene-reconstruction/paper-pack
REST build passport API/api/v1/paper/messykitchens-contact-rich-object-level-3d-scene-reconstruction/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

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}

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/messykitchens-contact-rich-object-level-3d-scene-reconstruction

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
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

MessyKitchens: Contact-rich object-level 3D scene reconstruction

Canonical ID messykitchens-contact-rich-object-level-3d-scene-reconstruction | Route /paper/messykitchens-contact-rich-object-level-3d-scene-reconstruction

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/messykitchens-contact-rich-object-level-3d-scene-reconstruction

MCP example

{
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}

source_context

{
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Buildability Receipt

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

Paper proof page receipt window

Watch and verify: MessyKitchens: Contact-rich object-level 3D scene reconstruction

/buildability/messykitchens-contact-rich-object-level-3d-scene-reconstruction

Watchwatch

Subject: MessyKitchens: Contact-rich object-level 3D scene reconstruction

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/messykitchens-contact-rich-object-level-3d-scene-reconstruction

Paper ref

messykitchens-contact-rich-object-level-3d-scene-reconstruction

arXiv id

2603.16868

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

52469aea8e4d631dbad08687fc939522a8fa6508fa3aec3d7942f8b69ed3901b

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.

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MessyKitchens: Contact-rich object-level 3D scene reconstruction

MessyKitchens: Contact-rich object-level 3D scene reconstruction

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