LiDAR Datasets for Autonomous Systems and 3D Perception
High-density point cloud datasets with 3D object annotations, semantic segmentation, and sensor fusion. Engineered for your LiDAR hardware and target environment.
20+
Years in AI training data
1,000+
Language locales
1M+
Hours of video annotated
ISO
27001 certified
The Challenge
Beyond Public Point Cloud Benchmarks
KITTI, nuScenes, and Waymo Open Dataset enabled early 3D perception research. Production LiDAR systems need environment-specific, hardware-matched data.
LiDAR models trained on benchmark datasets fail in deployment when the sensor hardware differs, the environment changes, or rare obstacles appear. Each LiDAR model produces a distinct point cloud density and pattern that models learn from as much as the physical scene.
Public LiDAR datasets suffer from sensor homogeneity (primarily Velodyne HDL-64) and environment bias (North American and European roads), leaving models unprepared for different hardware configurations and global deployment environments.
LXT captures custom LiDAR datasets with your specific sensor hardware in your target environment. We deliver calibrated multi-return point clouds, synchronized camera data, and precise 3D annotations matched to the exact conditions your system will encounter.
Why Teams Upgrade
Limitations of Public LiDAR Datasets
Standard benchmarks serve research well. Production deployments need more.
| Dataset | Primary Limitation | Impact |
|---|---|---|
| KITTI LiDAR | Velodyne HDL-64 only; Karlsruhe Germany environment; no adverse weather splits | Hardware lock |
| nuScenes | Velodyne HC-32 only; Singapore and Boston; 1,000 scenes insufficient for rare events | Limited scale |
| Waymo Open | Waymo proprietary sensor only; not transferable to other sensor configs | Proprietary sensor |
| SemanticKITTI | Single sensor, single city; sequential odometry only; limited object diversity | Narrow scope |
| PandaSet | Pandar64 sensor only; San Francisco driving; limited environmental diversity | Geographic bias |
Not sure which specs you need?
Our data specialists help you scope the right dataset for your model architecture.
Configurable Specifications
Specs Built Around Your Model
Public datasets come fixed. Yours is configured for your architecture, environment, and use case.
Sensor Specifications
Hardware Coverage
- LiDAR Models: Velodyne (16-128 beam), Ouster (32-128), Hesai, Luminar, Innoviz
- Returns: Single, dual, and multi-return configurations with intensity and reflectivity
- Synchronization: Calibrated camera and radar sensor fusion with precise timestamping
Environment Types
Capture Diversity
- Outdoor Environments: Urban, suburban, rural, industrial, and port environments
- Indoor Environments: Warehouses, factories, hospitals, and retail environments
- Conditions: Clear, rain, fog, and dust with sensor degradation characterization
Annotation Types
3D Ground Truth
- 3D Bounding Boxes: Class, dimensions, heading, and tracking ID per object
- Semantic Segmentation: Per-point class labels: ground, vegetation, vehicle, pedestrian
- Instance Segmentation: Per-point instance IDs for multi-object separation
Need a custom configuration?
We've built datasets across dozens of domains and use cases. Let's scope yours.
Capturing Complexity
Edge Cases in 3D Perception
High-accuracy models handle rare attributes that public datasets miss.
Long-Range Sparse Returns
Distant objects with very few LiDAR returns challenge 3D detectors. Production systems must handle low-point-count detections reliably at range.
Adverse Weather Degradation
Rain drops, snow, and fog create false returns (ghost points) that cause spurious detections. Weather-characterized point clouds are essential for robust production models.
Sensor Occlusion Patterns
Objects partially behind walls, pillars, or other vehicles create incomplete point clouds. Occlusion-aware annotation with visibility ratings enables better uncertainty modeling.
Ground-Level and Small Objects
Curbs, debris, and low-profile obstacles near the sensor blind zone that cause failure modes in outdoor robot navigation and AV deployments.
Ground Truth Quality
Human-in-the-Loop Annotation
Precise annotation bridges raw data and learnable signal. Expert annotators deliver precision automated tools can't match.
3D Bounding Box Annotation
Precise cuboid annotations in sensor and world coordinate frames with class, tracking ID, velocity estimate, and occlusion level.
Point Semantic Labeling
Per-point class labels across ground surface, vegetation, static structures, vehicles, pedestrians, and free-space categories.
Surface and Depth Annotation
Ground plane estimation, height maps, and surface normal annotations for navigation and terrain classification models.
Industry Applications
LiDAR Datasets for Your Domain
Custom taxonomies and collection protocols for specific deployment contexts.
Autonomous Vehicles
Perception stack for self-driving systems
Mobile Robotics
Indoor and outdoor navigation
HD Mapping
3D infrastructure and road network mapping
Smart Cities
Traffic monitoring and pedestrian flow
Industrial Automation
Warehouse robots, AGVs, forklift systems
Mining and Construction
Site monitoring, earthwork automation
Agriculture
Field mapping, crop monitoring, harvesting
Infrastructure Inspection
Bridge, tunnel, and utility scanning
Compliance & Ethics
Secure and Ethical Data Collection
Data collection involving people and sensitive content requires robust security, compliance, and ethical protocols at every stage.
Global Demographic Reach
Collection across 1,000+ locales and diverse demographics to prevent algorithmic bias in your deployed models.
ISO 27001 Certified
Sensitive projects processed in certified secure facilities meeting the highest information security standards.
GDPR & Privacy Compliance
All collection and annotation protocols vetted for consent and privacy. Legally robust for global deployment.
Frequently Asked Questions
LiDAR Dataset FAQs
Which LiDAR sensors do you support?+
We work with Velodyne (VLP-16 to HDL-128), Ouster (OS0-128), Hesai (Pandar series), Luminar Iris, Innoviz Pro, and Continental ARS LiDARs. We can configure collection with your exact sensor model and mounting position.
Can you capture indoor environments?+
Yes. We collect indoor environments including warehouses, factories, hospitals, and retail spaces. Indoor collection uses the same calibration and annotation workflows as outdoor, with additional ground plane and ceiling annotation options.
How do you handle sensor-specific annotation?+
Annotations are delivered in the sensor coordinate frame of your hardware with transformation matrices to world coordinates. We match nuScenes, KITTI, or custom annotation schemas as needed.
Can you characterize adverse weather effects?+
Yes. We collect in rain, snow, and fog conditions and can provide weather metadata tags per frame. For controlled weather characterization, we use environmental chambers to produce repeatable conditions.
What formats do you deliver?+
KITTI .bin, nuScenes JSON, ROS bag, and custom binary formats. Point clouds include x, y, z, intensity, ring index, and return number. Camera images are synchronized with LiDAR timestamps and calibration matrices included.
What does a custom LiDAR dataset cost?+
Projects range from $30K for focused environment datasets (10,000-50,000 frames) to $200K+ for large multi-environment, multi-sensor campaigns with adverse weather coverage. Detailed quotes follow a scoping call.
Get Started
Scope Your Custom LiDAR Dataset
Share your sensor hardware, target environment, and annotation requirements. Our 3D perception data specialists will provide a detailed proposal within 48 hours.
Please provide us with the details of your inquiry and one of our team members will be in touch.
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