JRM: Joint Reconstruction Model for Multiple Objects without Alignment

JRM: Joint Reconstruction Model for Multiple Objects without Alignment | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/jrm-joint-reconstruction-model-for-multiple-objects-without-alignment

stale

Proof freshness: stale
Proof status: unverified
Display score: 4/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 68
Source count: 3
Coverage: 50%

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

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Canonical ID jrm-joint-reconstruction-model-for-multiple-objects-without-alignment | Route /signal-canvas/jrm-joint-reconstruction-model-for-multiple-objects-without-alignment

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MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 7

References: 68

Proof: Verification pending

Freshness state: computing

Source paper: JRM: Joint Reconstruction Model for Multiple Objects without Alignment

PDF: https://arxiv.org/pdf/2603.25985v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T21:59:10.055Z

Signal Canvas receipt window

Not build-ready: JRM: Joint Reconstruction Model for Multiple Objects without Alignment

/buildability/jrm-joint-reconstruction-model-for-multiple-objects-without-alignment

Ignoreblocked

Subject: JRM: Joint Reconstruction Model for Multiple Objects without Alignment

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
JRM is a 3D flow-matching generative model that implicitly aggregates unaligned observations in its latent space, learning to produce consistent and faithful reconstructions in a data-driven manner without explicit constraints.
Implicationpartial
This is a core claim stated directly in the abstract and elaborated on in the method section.
Verificationpartial
partial
Evidencepartial
Evaluations on synthetic and real-world data show that JRM's implicit aggregation removes the need for explicit alignment, improves robustness to incorrect associations, and naturally handles non-rigid changes such as articulation.
Implicationpartial
This claim is explicitly stated in the abstract and supported by experimental results shown in figures and tables.
Verificationpartial
partial
Evidencepartial
Overall, JRM outperforms both independent and alignment-based baselines in reconstruction quality.
Implicationpartial
This is a direct claim from the abstract, supported by quantitative results presented in Table 2 and qualitative results in Figure 5 and 8.
Verificationpartial
partial
Evidencepartial
We propose the Joint Reconstruction Model (JRM) to leverage repetition by framing object reconstruction as one of personalized generation... JRM extends ShapeR [31], a conditional 3D generative model that reconstructs objects individually, by introducing coupled object reconstruction.
Implicationpartial
The abstract and introduction clearly state JRM is a 3D flow-matching generative model and its extension from ShapeR.
Verificationpartial
partial
Evidencepartial
Although reconstructions do deteriorate compared to independent reconstruction, JRM demonstrates improved robustness to mismatched, distractor objects. Figure 7
Implicationpartial
This is explicitly shown in Figure 7 and discussed in the text.
Verificationpartial
partial
Evidencepartial
Figure 6. Plot of reconstruction quality versus: alignment error (left); matching error(right). FM is sensitive to errors in both alignment and matching, while JRM is robust to these errors.
Implicationpartial
This claim is directly supported by Figure 6, which plots reconstruction quality against alignment and matching errors for both FM and JRM.
Verificationpartial
partial
Evidencepartial
While these additional priors improve visually appealing novel view synthesis, the resulting meshes often feature noisy geometry and the additional overhead means the per-scene NeRF-based optimization process takes hours to complete.
Implicationpartial
This is a specific performance metric mentioned when comparing to DPRecon.
Verificationpartial
partial

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JRM: Joint Reconstruction Model for Multiple Objects without Alignment

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