OVI-MAP:Open-Vocabulary Instance-Semantic Mapping

OVI-MAP:Open-Vocabulary Instance-Semantic Mapping | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/ovi-map-open-vocabulary-instance-semantic-mapping

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 85
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.

Agent Handoff

Canonical ID ovi-map-open-vocabulary-instance-semantic-mapping | Route /signal-canvas/ovi-map-open-vocabulary-instance-semantic-mapping

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/ovi-map-open-vocabulary-instance-semantic-mapping

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "ovi-map-open-vocabulary-instance-semantic-mapping",
    "query_text": "Summarize OVI-MAP:Open-Vocabulary Instance-Semantic Mapping"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "OVI-MAP:Open-Vocabulary Instance-Semantic Mapping",
  "normalized_query": "2603.26541",
  "route": "/signal-canvas/ovi-map-open-vocabulary-instance-semantic-mapping",
  "paper_ref": "ovi-map-open-vocabulary-instance-semantic-mapping",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 12

References: 85

Proof: Verification pending

Freshness state: computing

Source paper: OVI-MAP:Open-Vocabulary Instance-Semantic Mapping

PDF: https://arxiv.org/pdf/2603.26541v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T21:52:10.343Z

Signal Canvas receipt window

Watch and verify: OVI-MAP:Open-Vocabulary Instance-Semantic Mapping

/buildability/ovi-map-open-vocabulary-instance-semantic-mapping

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Subject: OVI-MAP:Open-Vocabulary Instance-Semantic Mapping

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

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
We introduce OVI-MAP that decouples instance reconstruction from semantic inference.
Implicationpartial
This is a core methodological contribution explicitly stated in the abstract and elaborated upon in the text.
Verificationpartial
partial
Evidencepartial
We propose to build a class-agnostic 3D instance map that is incrementally constructed from RGB-D input
Implicationpartial
This is a key aspect of the proposed method, clearly described in the abstract and illustrated in the figures.
Verificationpartial
partial
Evidencepartial
while semantic features are extracted only from a small set of automatically selected views using vision-language models.
Implicationpartial
This is a specific technical detail of the OVI-MAP method, highlighted in the abstract and detailed in the methodology.
Verificationpartial
partial
Evidencepartial
Our system operates in real time
Implicationpartial
Stated in the abstract and supported by performance metrics (e.g., 30 fps) in the results tables.
Verificationpartial
partial
Evidencepartial
and outperforms state-of-the-art open-vocabulary mapping baselines on standard benchmarks.
Implicationpartial
Explicitly stated in the abstract and supported by comparative results in Tables 2 and 3.
Verificationpartial
partial
Evidencepartial
Existing methods often rely on the closed-set assumption or dense per-pixel language fusion, which limits scalability and temporal consistency.
Implicationpartial
This is a limitation of prior work identified by the authors to motivate their approach.
Verificationpartial
partial
Evidencepartial
We achieve comparable or better accuracy to offline approaches while consistently outperforming prior online systems, particularly at high IoU thresholds (AP75).
Implicationpartial
This claim is directly supported by the quantitative results presented in Table 2.
Verificationpartial
partial
Evidencepartial
while semantic features are extracted only from a small set of automatically selected views using vision-language models.
Implicationpartial
This is a key technical innovation of OVI-MAP, differentiating it from methods that use dense per-pixel fusion.
Verificationpartial
partial
Evidencepartial
We introduce OVI-MAP that decouples instance reconstruction from semantic inference. We propose to build a class-agnostic 3D instance map that is incrementally constructed from RGB-D input, while semantic features are extracted only from a small set of automatically selected views using vision-language models. This design enables stable instance tracking and zero-shot semantic labeling throughout online exploration.
Implicationpartial
This is a core methodological claim explicitly stated in the abstract and elaborated upon in the text.
Verificationpartial
partial
Evidencepartial
Our system operates in real time and outperforms state-of-the-art open-vocabulary mapping baselines on standard benchmarks.
Implicationpartial
This is a key performance claim explicitly stated in the abstract and supported by performance metrics in the results tables.
Verificationpartial
partial
Evidencepartial
Our system operates in real time and outperforms state-of-the-art open-vocabulary mapping baselines on standard benchmarks.
Implicationpartial
This is a direct performance claim made in the abstract and substantiated by comparative results in the tables.
Verificationpartial
partial
Evidencepartial
We achieve comparable or better accuracy to offline approaches while consistently outperforming prior online systems, particularly at high IoU thresholds (AP75).
Implicationpartial
This claim is directly supported by the results presented in Table 2, which compares OVI-MAP to other methods on specific datasets and metrics.
Verificationpartial
partial

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

OVI-MAP:Open-Vocabulary Instance-Semantic Mapping

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