YOLOViz is a high-performance LiDAR and multi-sensor platform for validating high-quality computer vision systems. Combined with rProcess-managed data operations, annotation, and quality assurance, it provides a connected path from raw sensor data to validated output.
Tell us about your dataset and technical requirements.
YOLOViz is an on-premises LiDAR and multi-sensor annotation platform for preparing, reviewing and validating complex ADAS perception datasets.
It combines high-density point-cloud visualization, synchronized camera and radar context, annotation-quality workflows, AI-assisted pre-labeling and human-controlled quality review in one environment. Your teams can use the platform independently or combine it with rProcess-managed annotation and QA.
PLATFORM CAPABILITIES
From high-density point clouds to synchronized sensor context and AI-assisted pre-labeling, YOLOViz supports the workflows behind perception-data preparation and review.
Navigate high-density LiDAR datasets while maintaining the spatial context required for precise object review, annotation and validation.
Review synchronized LiDAR, camera, radar and odometry data within a unified scene for accurate object analysis and validation.
Run the full annotation workflow inside your own infrastructure, with no requirement to send sensitive recordings to a third-party cloud.
Accelerate repetitive data preparation with AI-assisted pre-labeling, while keeping human reviewers in control of corrections and final dataset quality.
YOLOViz is designed for environments where control over sensitive automotive data matters. The platform can support on-premises and restricted deployment models, subject to the customer’s approved architecture and technical requirements.
During technical discovery, rProcess can review deployment architecture, access requirements, infrastructure, security controls and the operating model for delivery teams working with customer data.
Run the platform within an approved customer-controlled infrastructure environment.
Support restricted environments where the approved deployment architecture permits operation without dependence on public internet access.
Define how data is accessed, processed, reviewed and governed across on-premises and private cloud environments according to customer security and operational requirements.
YOLOViz Core
Customer-controlled environment
Build tailored workflows that transform customer-specific sensor inputs into annotation, metadata and output formats required by downstream systems.
Multi-sensor data support
Visualization and Annotation Platform
Open and extensible interface
Three deployments demonstrate how YOLOViz handles dense sequential LiDAR, gigapixel geospatial imagery, and customer environments where data cannot leave the network.
A 1,200-frame autonomous-driving LiDAR dataset with nearly 50 million points per frame, made fully annotatable through a purpose-built, on-premises architecture.
Orthographic mapping imagery containing more than 2.4 billion pixels, made fully annotatable at sub-pixel precision through a tiled, multi-resolution architecture.
A fully on-premises deployment accessed entirely through the client's VDI, delivering high-performance annotation with zero data leaving the client network.
YOLOViz supports established formats used in automotive and point-cloud workflows for both data ingestion and label output. During discovery, the rProcess team can assess customer-specific formats, conversion requirements, and integration needs.
YOLOViz works with the recording and sensor formats already established in automotive and point-cloud workflows.
Export labels in the formats your pipeline already expects, without forcing a redesign of your existing workflow.
Extend YOLOViz to match the specific recording, conversion, or delivery requirements of your team.
Pilot Process
Evaluate YOLOViz against your real sensor-data requirements, deployment environment and delivery model. The pilot brings technical discovery, secure data access, visualization, annotation, review, QA and validated outputs into one structured evaluation.
Review your sensors, data formats, annotation workflow, security controls and performance requirements.
Run a representative sequence through YOLOViz to evaluate visualization, synchronization and review performance.
Confirm deployment architecture, GPU and hardware requirements, integrations and access controls.
Move into a controlled rollout with operating guidance, quality checks and rProcess delivery support.
Evaluation · How YOLOViz Stands Apart
There is no single deployment model for every ADAS programe. The right approach depends on data volume, security requirements, internal capacity, collaboration needs, infrastructure and programe timelines. YOLOViz combines high-performance LiDAR and multi-sensor workflows with flexible deployment, adaptable pipelines and managed ADAS data services.
TRUST · SECURITY · COMPLIANCE
Our operating environment is built around recognized quality, information-security, responsible-business and data-protection requirements.
Quality Management
System
Information Security
Management
Responsible Business Alliance Standards
Automotive Security &
Privacy
Data Privacy &
Protection
FAQ · Frequently asked questions
Have a specific operating-system, GPU, data-format, deployment or integration requirement? Discuss it with the YOLOViz team during technical discovery.
YOLOViz is an on-premises LiDAR and multi-sensor annotation platform for visualizing, annotating, reviewing and validating complex ADAS perception datasets. It is purpose-built for high-density point clouds, long sensor sequences and synchronized camera, radar and LiDAR data.
YOLOViz is designed for LiDAR point clouds plus synchronized camera and radar data used in perception and sensor-fusion workflows.
rProcess can provide domain-trained annotation teams, review, QA and scalable data-operations capacity alongside the YOLOViz platform. The operating model is defined during discovery.
A pilot can cover discovery and data assessment, an agreed dataset and success criteria, platform configuration, managed execution and QA, results review and a path to commercial scale-up.
YOLOViz supports ADTF, ROS Bag, PCD, KITTI-style layouts, Ibeo IDC, RTMaps and other approved input structures. It can export XML, JSON, ASAM OpenLABEL, COCO and supported YOLO formats. Customer-specific requirements can be assessed during discovery.
YOLOViz is designed to support on-premises and restricted deployment models. The exact architecture, access controls and delivery approach should be validated for the customer's environment.
The solution can combine YOLOViz review workflows with rProcess-managed annotation, multi-level QA and delivery controls. Specific quality metrics and acceptance criteria are agreed for the program.
YOLOViz is designed specifically for high-density LiDAR, long sensor sequences and synchronized ADAS data. It also supports customer-controlled deployment and can be combined with rProcess-managed annotation and QA.
Yes. YOLOViz can support annotation review, pre-label correction and dataset validation without requiring rProcess to perform the complete annotation program.
Yes. rProcess can recommend a delivery model based on the required workload, timeline, quality criteria and domain expertise. Team capacity and ramp-up requirements are confirmed during discovery.
Start with your use case, sensor types, approximate data volume, quality expectations, security or deployment constraints, internal team capacity and target timeline. A dataset upload is not required for the initial conversation.
Select “Upload a Sample Dataset” or “Request a Live Demo” on this page. The rProcess team will review your requirements and recommend an appropriate evaluation path.
Discuss your sensor types, data volume, sequence length, quality requirements and deployment constraints with the YOLOViz team. A dataset upload is not required for the initial conversation.
