ARXIV:2603.16816 · 3D PERCEPTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation

Q: What products could be built from this research?

Why now — increasing focus on biodiversity and climate change drives demand for better wildlife tech, while advances in affordable LiDAR and AI make multimodal systems more accessible, and regulatory pressures for animal safety in agriculture and autonomous vehicles create a ripe market.

Q: What are the practical use cases?

A real-time monitoring system for national parks that uses RGB-LiDAR fusion to track animal movements in 3D, alerting rangers to poaching activities or animal distress with higher fidelity than current camera-only solutions.

Muhammad Aamir · Naoya Muramatsu · Sangyun Shin · Matthew Wijers · Jiaxing Jhong · Xinyu Hou · +2 at arXiv

WildDepth is a multimodal dataset designed to enhance depth estimation and 3D reconstruction for wildlife perception.

Blocked on Code›Score4.0Evidence unverified

Opportunity summary

Pain WildDepth is a multimodal dataset designed to enhance depth estimation and 3D reconstruction for wildlife perception.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

WildDepth is a multimodal dataset designed to enhance depth estimation and 3D reconstruction for wildlife perception. Starting from rigid objects with relatively simple geometric shapes, such as vehicles, the research has expanded to address…

METHOD

Full abstract

Depth estimation and 3D reconstruction have been extensively studied as core topics in computer vision. Starting from rigid objects with relatively simple geometric shapes, such as vehicles, the research has expanded to address general objects, including challenging deformable objects, such as humans and animals. However, for the animal, in particular, the majority of existing models are trained based on datasets without metric scale, which can help validate image-only models. To address this limitation, we present WildDepth, a multimodal dataset and benchmark suite for depth estimation, behavior detection, and 3D reconstruction from diverse categories of animals ranging from domestic to wild environments with synchronized RGB and LiDAR. Experimental results show that the use of multi-modal data improves depth reliability by up to 10% RMSE, while RGB-LiDAR fusion enhances 3D reconstruction fidelity by 12% in Chamfer distance. By releasing WildDepth and its benchmarks, we aim to foster robust multimodal perception systems that generalize across domains.

RESULT

ScienceToStartup currently rates this 4.0/10 on the public viability pass. Experimental results show that the use of multi-modal data improves depth reliability by up to 10% RMSE, while RGB-LiDAR fusion enhances 3D reconstruction fidelity…

WHY NOW

3D Perception moved forward this cycle; last verified April 2026. Public score 4.0/10.

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

Opportunity summary

Score4.0

PainWildDepth is a multimodal dataset designed to enhance depth estimation and 3D reconstruction for wildlife perception.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

WildDepth is a multimodal dataset designed to enhance depth estimation and 3D reconstruction for wildlife perception.

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

ARXIV:2603.16816 · 3D PERCEPTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation

Muhammad Aamir · Naoya Muramatsu · Sangyun Shin · Matthew Wijers · Jiaxing Jhong · Xinyu Hou · +2 at arXiv

WildDepth is a multimodal dataset designed to enhance depth estimation and 3D reconstruction for wildlife perception.

Blocked on Code›Score4.0Evidence unverified

Opportunity summary

Pain WildDepth is a multimodal dataset designed to enhance depth estimation and 3D reconstruction for wildlife perception.

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 Perception moved forward this cycle; last verified April 2026. Public score 4.0/10.

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

Opportunity summary

Score4.0

PainWildDepth is a multimodal dataset designed to enhance depth estimation and 3D reconstruction for wildlife perception.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

WildDepth is a multimodal dataset designed to enhance depth estimation and 3D reconstruction for wildlife perception.

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

Paper Pack

10.48550/arXiv.2603.16816

WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation

WildDepth is a multimodal dataset designed to enhance depth estimation and 3D reconstruction for wildlife perception.

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

4.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

3D Perception moved forward this cycle; last verified April 2026. Public score 4.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
WildDepth is a multimodal dataset designed to enhance depth estimation and 3D reconstruction for wildlife perception. Starting from rigid objects with relatively simple geometric shapes, such as vehicles, the research has expanded to address general objects, including challenging deformable objects, such as humans and animals.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Depth estimation and 3D reconstruction have been extensively studied as core topics in computer vision. Starting from rigid objects with relatively simple geometric shapes, such as vehicles, the research has expanded to address general objects, including challenging deformable objects, such as humans and animals.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Experimental results show that the use of multi-modal data improves depth reliability by up to 10% RMSE, while RGB-LiDAR fusion enhances 3D reconstruction fidelity by 12% in Chamfer distance.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
3D Perception moved forward this cycle; last verified April 2026. Public score 4.0/10.
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

3D Perception

Competitors

not indexed

Competitive landscape

WildDepth is a multimodal dataset designed to enhance depth estimation and 3D reconstruction for wildlife perception.

Segment

3D Perception

Adoption evidence

No public code link in the paper record yet

Commercial read

4.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.16816 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)

MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details

2025Ruicheng Wang, Sicheng Xu et al.

MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps

2025Valentin Gabeff, Haozhe Qi et al.

Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

2024Alexey Bochkovskiy, Amaël Delaunoy et al.

WildPose: A Long-Range 3D Wildlife Motion Capture System

2024Naoya Muramatsu, Sangyun Shin et al.

An Improved MonoDepth2 Algorithm for Vehicle Monocular Depth Estimation

2024Chaoqun Li, Chenxi Yue et al.

BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos

2024Isla Duporge, Maksim Kholiavchenko et al.

Channel morphological change monitoring using high-resolution LiDAR-derived DEM and multi-temporal imageries.

2024T. Andualem, Stefan Peters et al.

Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering

2024Yutao Feng, Xiang Feng et al.

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

2024Lihe Yang, Bingyi Kang et al.

KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos

2024Maksim Kholiavchenko, Jenna Kline et al.

Making the most of existing data in conservation research

2023A. Binley, J. Vincent et al.

ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors

2023Xiaoxuan Ma, Stephan P. Kaufhold et al.

LoTE-Animal: A Long Time-span Dataset for Endangered Animal Behavior Understanding

2023Dan Liu, Jin Hou et al.

MammalNet: A Large-Scale Video Benchmark for Mammal Recognition and Behavior Understanding

2023Jun Chen, Ming Hu et al.

VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking

2023Limin Wang, Bingkun Huang et al.

Robot Operating System 2: Design, architecture, and uses in the wild

2022Steve Macenski, Tully Foote et al.

Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding

2022Xun Long Ng, Kian Eng Ong et al.

SuperAnimal pretrained pose estimation models for behavioral analysis

2022Shaokai Ye, Anastasiia Filippova et al.

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

2021Yifu Zhang, Pei Sun et al.

KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D

2021Yiyi Liao, Jun Xie et al.

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

none indexed

Extension

none indexed

Commercially relevant

Higher ViabilityDeepSight: Bridging Depth Maps and Language with a Depth-Driven Multimodal Model

7.0

Higher ViabilitySAM 3D Animal: Promptable Animal 3D Reconstruction from Images in the Wild

8.0

Higher Viability$D^3$-RSMDE: 40$\times$ Faster and High-Fidelity Remote Sensing Monocular Depth Estimation

7.0

Higher ViabilityV2U4Real: A Real-world Large-scale Dataset for Vehicle-to-UAV Cooperative Perception

7.0

Higher ViabilityLightweight Multimodal Adaptation of Vision Language Models for Species Recognition and Habitat Context Interpretation in Drone Thermal Imagery

8.0

Higher ViabilityGeoHeight-Bench: Towards Height-Aware Multimodal Reasoning in Remote Sensing

7.0

Higher ViabilitySpaceSense-Bench: A Large-Scale Multi-Modal Benchmark for Spacecraft Perception and Pose Estimation

8.0

Higher ViabilityPanoramic Multimodal Semantic Occupancy Prediction for Quadruped Robots

8.0

Higher ViabilityCross-Vehicle 3D Geometric Consistency for Self-Supervised Surround Depth Estimation on Articulated Vehicles

7.0

Higher ViabilityMetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

8.0

Conflicting

none indexed

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: 2603.16816
Route: /paper/wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation
Active tab: read
Artifact: wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation/paper-pack
REST build passport API/api/v1/paper/wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation/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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  "paper_id": "f296a352-ba2f-440d-9301-f4438d00fec8",
  "arxiv_id": "2603.16816",
  "canonical_route": "/paper/wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation",
  "endpoints": {
    "paper_pack": "/api/v1/paper/wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation/paper-pack",
    "build_passport": "/api/v1/paper/wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation/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/wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation

stale

Proof freshness: stale
Proof status: unverified
Display score: 4/10
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

WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation

Canonical ID wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation | Route /paper/wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.16816"
  }
}

source_context

{
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  "mode": "paper",
  "query": "WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation",
  "normalized_query": "2603.16816",
  "route": "/paper/wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation",
  "paper_ref": "wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation",
  "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

Not build-ready: WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation

/buildability/wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation

Ignoreblocked

Subject: WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation

Verdict

Ignore

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

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/wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation

Paper ref

wilddepth-a-multimodal-dataset-for-3d-wildlife-perception-and-depth-estimation

arXiv id

2603.16816

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

627d304ce3b82970c22f8027fc530df2b98bec25a3631fa66fd0f17eb228ed36

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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WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation

WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation

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