Evidence Receipt. Related Resources.
AsyncMDE: Real-Time Monocular Depth Estimation via Asynchronous Spatial Memory
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Verification pending
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Page Freshness
Signal Canvas proof surface
Canonical route: /signal-canvas/asyncmde-real-time-monocular-depth-estimation-via-asynchronous-spatial-memory
- 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.
Agent Handoff
AsyncMDE: Real-Time Monocular Depth Estimation via Asynchronous Spatial Memory
Canonical ID asyncmde-real-time-monocular-depth-estimation-via-asynchronous-spatial-memory | Route /signal-canvas/asyncmde-real-time-monocular-depth-estimation-via-asynchronous-spatial-memory
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/asyncmde-real-time-monocular-depth-estimation-via-asynchronous-spatial-memoryMCP example
{
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"paper_ref": "asyncmde-real-time-monocular-depth-estimation-via-asynchronous-spatial-memory",
"query_text": "Summarize AsyncMDE: Real-Time Monocular Depth Estimation via Asynchronous Spatial Memory"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "AsyncMDE: Real-Time Monocular Depth Estimation via Asynchronous Spatial Memory",
"normalized_query": "2603.10438",
"route": "/signal-canvas/asyncmde-real-time-monocular-depth-estimation-via-asynchronous-spatial-memory",
"paper_ref": "asyncmde-real-time-monocular-depth-estimation-via-asynchronous-spatial-memory",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Preparing verified analysis
Dimensions overall score 8.0
GitHub Code Pulse
No public code linked for this paper yet.
Claim map
- Evidencepartial
This paper presents AsyncMDE, an asynchronous depth perception system consisting of a foundation model and a lightweight model that amortizes the foundation model's computational cost over time.
ImplicationpartialThe abstract explicitly describes AsyncMDE as an 'asynchronous depth perception system' designed to 'amortize the foundation model's computational cost over time'.
Verificationpartialpartial
- Evidencepartial
This enables cross-frame feature reuse with bounded accuracy degradation.
ImplicationpartialThe abstract directly states this capability of AsyncMDE.
Verificationpartialpartial
- Evidencepartial
At a mere 3.83M parameters, it operates at 237 FPS on an RTX 4090
ImplicationpartialThe abstract provides specific performance metrics (FPS) and model size (parameters) for a given hardware platform.
Verificationpartialpartial
- Evidencepartial
recovering 77% of the accuracy gap to the foundation model while achieving a 25X parameter reduction.
ImplicationpartialThe abstract quantifies the accuracy recovery and parameter reduction achieved by AsyncMDE compared to the foundation model.
Verificationpartialpartial
- Evidencepartial
achieves 161FPS on a Jetson AGX Orin with TensorRT
ImplicationpartialThe abstract provides a specific performance metric (FPS) for a different edge hardware platform with optimization.
Verificationpartialpartial
- Evidencepartial
clearly demonstrating its feasibility for real-time edge deployment.
ImplicationpartialThe abstract explicitly concludes that the performance on edge hardware demonstrates feasibility for real-time deployment.
Verificationpartialpartial
- Evidencepartial
yet its computational cost often prohibits deployment on edge platforms.
ImplicationpartialThe abstract identifies this as a problem that AsyncMDE aims to solve, indicating it's a known limitation of prior work.
Verificationpartialpartial
