D3Scenes (D3S) 2D & 3D Annotations for Open-Source Driving Datasets
D3Scenes (D3S) 2D & 3D Annotations for Open-Source Driving Datasets
Accelerate your computer vision pipeline with benchmark-quality data designed for model-readiness. D3Scenes delivers precision, consistency, and contextual intelligence at scale.
The D3Scenes is an open-source dataset that consists of 2D and 3D annotations on top of open-source autonomous/automated driving large-scale datasets such as A2D2 and Argoverse.
The A2D2 dataset (Reference Links: ReadMe, License) was collected in Germany by Audi AG, specifically in and around several cities (Ingolstadt, Munich, and surrounding areas) where Audi conducts autonomous driving research and testing. The driving environment includes urban, suburban, rural, and highway scenes.
The Argoverse dataset (Reference Links: Terms of Use, Privacy Policy) comes from six U.S. cities with complex, unique driving environments: Miami, Austin, Washington DC, Pittsburgh, Palo Alto, and Detroit.
License: D3S is available for non-commercial use under CC BY-NC-SA 4.0 (Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International).
Build Smarter AI Systems with DDD’s D3Scenes
D3S datasets combine pixel-perfect 2D and LiDAR-accurate 3D annotations with contextual intelligence, identifying not just what an object is, but who and why it matters.
2D Annotations:
Bounding Box
Image Segmentation
3D Annotations:
iDAR Bounding Box
Object Attributes:
Pedestrians (VRUs)
Vehicles
Traffic Signs
Annotation Volume
1,074
A2D2 (2D)
files annotated
with segmentation + bounding boxes (primarily suburban).
with segmentation + bounding boxes (primarily suburban).
1,199
Argoverse (2D)
files annotated
with segmentation + bounding boxes (busy urban, highways, suburban).
with segmentation + bounding boxes (busy urban, highways, suburban).
790
Argoverse (3D)
Files Annotated
with LiDAR 3D bounding boxes.
with LiDAR 3D bounding boxes.
95%
Accuracy Across
2D boxes, semantic segmentation, and object attributes (validated through DDD’s multi-stage QA).
2D boxes, semantic segmentation, and object attributes (validated through DDD’s multi-stage QA).
Object Type Distribution
| Dataset | Annotation Type | Cars (%) | Vegetation (%) | Other Static Objects (%) | Sidewalk (%) |
|---|---|---|---|---|---|
| A2D2 | Segmentation42.4 | 17.6 | 21.4 | - | |
| A2D2 | Bounding Box | 30.7 | 18.9 | 34.9 | - |
| Argoverse | Segmentation | 27.4 | 24.9 | - | 8.36 |
| Argoverse | Bounding Box | 19.9 | 17.5 | 30.0 | - |
| Argoverse | 3D | 8.9 | 43.8 | 41.8 | - |
Why DDD (D3S) Stands Out
Precision + Context
Geometric accuracy and rich attributes (role, demographic, function) enable context-aware perception and planning, not just detection.
Diverse ODD Coverage
Annotations span suburban, urban, and highway to bolster generalization and robustness.
High Quality Standards
≥95% quality threshold enforced by rigorous QA, matching stringent, safety-critical requirements.
Actionable Intelligence
Go beyond “what” and “where” to capture “who” and “why” signals that improve decision-making for safer, smarter AD/ADAS systems.
DDD’s Unique Value Proposition
- Precision + Context Geometric accuracy and rich attributes (role, demographic, function) enable context-aware perception and planning, not just detection.
- Diverse ODD Coverage Annotations span suburban, urban, and highway to bolster generalization and robustness.
- High Quality Standards
≥95% quality threshold enforced by rigorous QA, matching stringent, safety-critical requirements.
- Actionable Intelligence
Go beyond “what” and “where” to capture “who” and “why” signals that improve decision-making for safer, smarter AD/ADAS systems.
Access Datasets Now
Turn complex data into smarter AI systems.
Talk to our Solutions Engineers to tailor datasets, enhance annotations, and accelerate your next AI innovation.
FAQs
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Vision-Language-Action (VLA) models integrate computer vision, natural language processing, and action reasoning to enable robots to perceive, comprehend, and interact with their surroundings.
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These models allow robots to interpret visual inputs, understand verbal instructions, and execute context-appropriate actions, making them more autonomous and intelligent.
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VLA models power innovations across autonomous driving, industrial automation, assistive robotics, and intelligent home systems.
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While conventional AI focuses on isolated tasks, VLA models combine visual understanding, language interpretation, and action generation into one unified framework, enabling a more human-like interaction with the environment.
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We handle vision, language, and sensor data, including RGB, depth, LiDAR, audio, simulation traces, and telemetry, all synchronized for multimodal alignment.
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Both. DDD supports simulation-to-real workflows, collecting and validating data across simulated environments and physical deployments to improve policy transfer.
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Yes. We design closed-loop validation sets that connect perception, policy, and action outcomes, enabling accurate performance evaluation and retraining.
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Our multi-pass QA and gold-standard reviewer training ensure precision levels exceeding industry benchmarks for multimodal labeling.
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Most pilots are completed within 4–6 weeks, including scoping, sample annotation, QA review, and performance validation before scaling to production.
Turn complex data into smarter AI systems.
Talk to our Solutions Engineers to tailor datasets, enhance annotations, and accelerate your next AI innovation.