ARXIV:2603.14701 · DEPTH COMPLETION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

AURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild

Q: What products could be built from this research?

Why now — the autonomous vehicle and robotics markets are scaling but hitting weather limitations; regulators are demanding better all-weather performance, and sensor fusion tech is mature enough to leverage this research without hardware overhaul.

Q: What are the practical use cases?

A cloud-based API that processes real-time sensor data from autonomous delivery robots in urban environments, providing denoised depth maps during rain or snow to prevent collisions and ensure uninterrupted service for logistics companies like Amazon or FedEx.

arXiv

AURORA-KITTI provides a robust solution for depth completion and denoising across diverse weather conditions.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain AURORA-KITTI provides a robust solution for depth completion and denoising across diverse weather conditions.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

AURORA-KITTI provides a robust solution for depth completion and denoising across diverse weather conditions. In this paper, we introduce AURORA-KITTI, the first large-scale multi-modal, multi-weather benchmark for robust depth completion in the wild.

METHOD

Full abstract

Robust depth completion is fundamental to real-world 3D scene understanding, yet existing RGB-LiDAR fusion methods degrade significantly under adverse weather, where both camera images and LiDAR measurements suffer from weather-induced corruption. In this paper, we introduce AURORA-KITTI, the first large-scale multi-modal, multi-weather benchmark for robust depth completion in the wild. We further formulate Depth Completion and Denoising (DCD) as a unified task that jointly reconstructs a dense depth map from corrupted sparse inputs while suppressing weather-induced noise. AURORA-KITTI contains over \textit{82K} weather-consistent RGBL pairs with metric depth ground truth, spanning diverse weather types, three severity levels, day and night scenes, paired clean references, lens occlusion conditions, and textual descriptions. Moreover, we introduce DDCD, an efficient distillation-based baseline that leverages depth foundation models to inject clean structural priors into in-the-wild DCD training. DDCD achieves state-of-the-art performance on AURORA-KITTI and the real-world DENSE dataset while maintaining efficiency. Notably, our results further show that weather-aware, physically consistent data contributes more to robustness than architectural modifications alone. Data and code will be released upon publication.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. DDCD achieves state-of-the-art performance on AURORA-KITTI and the real-world DENSE dataset while maintaining efficiency.

WHY NOW

Depth Completion 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

PainAURORA-KITTI provides a robust solution for depth completion and denoising across diverse weather conditions.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

AURORA-KITTI provides a robust solution for depth completion and denoising across diverse weather conditions.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.14701 · DEPTH COMPLETION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

AURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild

arXiv

AURORA-KITTI provides a robust solution for depth completion and denoising across diverse weather conditions.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain AURORA-KITTI provides a robust solution for depth completion and denoising across diverse weather conditions.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. DDCD achieves state-of-the-art performance on AURORA-KITTI and the real-world DENSE dataset while maintaining efficiency.

WHY NOW

Depth Completion 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

PainAURORA-KITTI provides a robust solution for depth completion and denoising across diverse weather conditions.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

AURORA-KITTI provides a robust solution for depth completion and denoising across diverse weather conditions.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.14701

AURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild

AURORA-KITTI provides a robust solution for depth completion and denoising across diverse weather conditions.

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 fallback30 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

ScienceToStartup currently rates this 7.0/10 on the public viability pass. DDCD achieves state-of-the-art performance on AURORA-KITTI and the real-world DENSE dataset while maintaining efficiency.

WHY NOW

Depth Completion 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
AURORA-KITTI provides a robust solution for depth completion and denoising across diverse weather conditions. In this paper, we introduce AURORA-KITTI, the first large-scale multi-modal, multi-weather benchmark for robust depth completion in the wild.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Robust depth completion is fundamental to real-world 3D scene understanding, yet existing RGB-LiDAR fusion methods degrade significantly under adverse weather, where both camera images and LiDAR measurements suffer from weather-induced corruption. In this paper, we introduce AURORA-KITTI, the first large-scale multi-modal, multi-weather benchmark for robust depth completion in the wild.
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. DDCD achieves state-of-the-art performance on AURORA-KITTI and the real-world DENSE dataset while maintaining efficiency.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Depth Completion 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

Depth Completion

Competitors

not indexed

Competitive landscape

AURORA-KITTI provides a robust solution for depth completion and denoising across diverse weather conditions.

Segment

Depth Completion

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 2603.14701 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(30)

Reference metadata pending (60c78bb0ba0c10f5b03d148ced8bcaf395608c64)

Reference metadata pending (7b3db8dfdaebb6d60c4bfef7ff6678cfacd20061)

Reference metadata pending (d756f116ae379ec7a56a79d4987fa3427264dbfb)

Reference metadata pending (edb79171cb28286a5acde5f82cdd045ca059c997)

Reference metadata pending (a8b59f2be5e78a3ae266a90a0b831a8b0bccd634)

Reference metadata pending (f712c94c1937374d918cef7c23f2ea211711eb83)

Reference metadata pending (7cdf0cf9f984516dc5c055767f213df0c0bca6fb)

Reference metadata pending (0f51efaf5d1da8adda41a4e37915038d1d764534)

Reference metadata pending (fa0af8d1ab092ccf4c961f4965684cc9d7a379b0)

Reference metadata pending (4f85517dd39f27fa8f7c31a77d70c8015d6a2cf6)

Reference metadata pending (8643b78c0588aa5cac6b077bfb77726ae0f843ea)

Reference metadata pending (13c28fd368cb2359034adedc58ef676e98d3de30)

Reference metadata pending (3214c6b4bfebe67794c9ca91c7a703d956709479)

Reference metadata pending (d5ab24beec066610a0af109e25913dba3e42a978)

Reference metadata pending (3704223766a540d83eab4a65c024297fc5ad8ad7)

Reference metadata pending (72d0550d6027dfa8c0ec26c6ceb61413ea4a6373)

Reference metadata pending (25a5f537c202c37de8e920930eb90623b15b7979)

Reference metadata pending (e9edc2d44af422cb6b8d8ce494161c7779ba0895)

Reference metadata pending (fe3eb5bdd6aec1633efaf9f71fba21aeae48234a)

Reference metadata pending (2be34bae73c7e41b44d313501a61557d572f24da)

Showing 20 of 30 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 WorkUniDA3D: A Unified Domain-Adaptive Framework for Multi-View 3D Object Detection

7.0

Prior WorkUnified Removal of Raindrops and Reflections: A New Benchmark and A Novel Pipeline

7.0

Prior Work4DRC-OCC: Robust Semantic Occupancy Prediction Through Fusion of 4D Radar and Camera

7.0

Prior WorkUENR-600K: A Large-Scale Physically Grounded Dataset for Nighttime Video Deraining

7.0

Extension

Builds On ThisWildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation

4.0

Builds On ThisLRC-WeatherNet: LiDAR, RADAR, and Camera Fusion Network for Real-time Weather-type Classification in Autonomous Driving

6.0

Builds On ThisFSKD: Monocular Forest Structure Inference via LiDAR-to-RGBI Knowledge Distillation

5.0

Builds On ThisATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity

0.0

Commercially relevant

Higher ViabilityAny to Full: Prompting Depth Anything for Depth Completion in One Stage

8.0

Higher ViabilityAW-MoE: All-Weather Mixture of Experts for Robust Multi-Modal 3D Object Detection

9.0

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: 2603.14701
Route: /paper/aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild
Active tab: read
Artifact: aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild/paper-pack
REST build passport API/api/v1/paper/aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild/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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  "canonical_route": "/paper/aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild",
  "endpoints": {
    "paper_pack": "/api/v1/paper/aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild/paper-pack",
    "build_passport": "/api/v1/paper/aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild/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/aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild

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

AURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild

Canonical ID aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild | Route /paper/aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.14701"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "AURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild",
  "normalized_query": "2603.14701",
  "route": "/paper/aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild",
  "paper_ref": "aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild",
  "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: AURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild

/buildability/aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild

Watchwatch

Subject: AURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild

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/aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild

Paper ref

aurora-kitti-any-weather-depth-completion-and-denoising-in-the-wild

arXiv id

2603.14701

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

75546b7f626e0c233aefebce594696442641332d84ca5aebf7fc2713d512cf3f

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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AURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild

AURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild

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