# Lidar

Published articles for Lidar.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps

DevFeed: [Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps](<https://devfeed.tech/articles/integrate-nvidia-omniverse-rtx-sensor-simulation-into-existing-apps-6867.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/integrate-nvidia-omniverse-rtx-sensor-simulation-into-existing-apps/>)

Author: Tanya Lenz

Published: 2026-07-20T15:00:00Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [SDKs](<https://devfeed.tech/topics/sdks.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [agent-skill](<https://devfeed.tech/tags/agent-skill.md>), [apis](<https://devfeed.tech/tags/apis.md>), [apps](<https://devfeed.tech/tags/apps.md>), [autonomous-vehicles](<https://devfeed.tech/tags/autonomous-vehicles.md>), [c](<https://devfeed.tech/tags/c.md>), [featured](<https://devfeed.tech/tags/featured.md>), [lidar](<https://devfeed.tech/tags/lidar.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [omniverse](<https://devfeed.tech/tags/omniverse.md>), [openusd](<https://devfeed.tech/tags/openusd.md>), [physical-ai](<https://devfeed.tech/tags/physical-ai.md>), [python](<https://devfeed.tech/tags/python.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>)

### AI overview

The article explains how to integrate NVIDIA Omniverse's ovrtx RTX sensor-simulation library into existing applications. It covers using its C and Python SDK to generate camera, lidar, radar, and related sensor outputs from OpenUSD scenes.

### Source excerpt

Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and...

## Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools

DevFeed: [Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools](<https://devfeed.tech/articles/optimizing-a-neural-reconstruction-pipeline-using-nvidia-nsight-developer-tools-6918.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/optimizing-a-neural-reconstruction-pipeline-using-nvidia-nsight-developer-tools/>)

Author: Tanya Lenz

Published: 2026-06-30T16:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Omniverse](<https://devfeed.tech/topics/omniverse.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Tooling](<https://devfeed.tech/topics/tooling.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [Physical AI](<https://devfeed.tech/topics/physical-ai.md>), [Robotics](<https://devfeed.tech/topics/robotics.md>), [Synthetic Data Generation](<https://devfeed.tech/topics/synthetic-data-generation.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [ai](<https://devfeed.tech/tags/ai.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [autonomous-vehicles](<https://devfeed.tech/tags/autonomous-vehicles.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [developer-tools](<https://devfeed.tech/tags/developer-tools.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [driving](<https://devfeed.tech/tags/driving.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [lidar](<https://devfeed.tech/tags/lidar.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [omniverse](<https://devfeed.tech/tags/omniverse.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [physical-ai](<https://devfeed.tech/tags/physical-ai.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [synthetic-data-generation](<https://devfeed.tech/tags/synthetic-data-generation.md>)

### AI overview

This article explains how NVIDIA Nsight Developer Tools can optimize the NVIDIA Omniverse NuRec neural reconstruction pipeline. It focuses on reducing GPU-intensive reconstruction and rendering costs to improve engineering iteration and move toward real-time performance.

### Source excerpt

NVIDIA Omniverse NuRec is a neural reconstruction pipeline for building high-fidelity 3D representations of real-world environments from multisensor data such...

## Powering self-driving vehicle analytics at Avride with ClickHouse Cloud

DevFeed: [Powering self-driving vehicle analytics at Avride with ClickHouse Cloud](<https://devfeed.tech/articles/powering-self-driving-vehicle-analytics-at-avride-with-clickhouse-cloud-4971.md>)

Original publisher: [Read original article](<https://clickhouse.com/blog/avride>)

Author: ClickHouse

Published: 2026-05-11T00:00:00Z

Content type: article

Language: en

Sources: [ClickHouse Blog](<https://devfeed.tech/sources/clickhouse-blog.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [Apache Iceberg](<https://devfeed.tech/topics/apache-iceberg.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [Grafana](<https://devfeed.tech/topics/grafana.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [apache-iceberg](<https://devfeed.tech/tags/apache-iceberg.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [aws](<https://devfeed.tech/tags/aws.md>), [clickhouse](<https://devfeed.tech/tags/clickhouse.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [data-infrastructure](<https://devfeed.tech/tags/data-infrastructure.md>), [driving](<https://devfeed.tech/tags/driving.md>), [grafana](<https://devfeed.tech/tags/grafana.md>), [latency](<https://devfeed.tech/tags/latency.md>), [lidar](<https://devfeed.tech/tags/lidar.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [robots](<https://devfeed.tech/tags/robots.md>), [storage](<https://devfeed.tech/tags/storage.md>), [streams](<https://devfeed.tech/tags/streams.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>), [tooling](<https://devfeed.tech/tags/tooling.md>), [visualization](<https://devfeed.tech/tags/visualization.md>)

### AI overview

Avride uses ClickHouse Cloud as the data backbone for its autonomous vehicles and delivery robots, supporting ride-data indexing, metrics, analytics, and internal tooling. Its migration from Apache Iceberg reduced index lookup and ingestion latency.

### Source excerpt

Avride replaced Apache Iceberg with ClickHouse Cloud, cutting index lookup latency from 20 seconds to under 100ms and ingestion from hours to seconds.

## Metric Depth from a Single Camera: How Robots See Without LiDAR

DevFeed: [Metric Depth from a Single Camera: How Robots See Without LiDAR](<https://devfeed.tech/articles/metric-depth-from-a-single-camera-how-robots-see-without-lidar-39657.md>)

Original publisher: [Read original article](<https://www.gauravsarma.com/posts/2026-04-07_metric-depth-from-a-single-camera>)

Published: 2026-04-07T00:00:00Z

Content type: tutorial

Language: en

Sources: [Gaurav Sarma's Blog](<https://devfeed.tech/sources/gaurav-sarma-s-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Point cloud](<https://devfeed.tech/topics/point-cloud.md>), [3D](<https://devfeed.tech/topics/3d.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [ai](<https://devfeed.tech/tags/ai.md>), [algorithm](<https://devfeed.tech/tags/algorithm.md>), [camera](<https://devfeed.tech/tags/camera.md>), [lidar](<https://devfeed.tech/tags/lidar.md>)

### AI overview

This article explains how robots can estimate dense, metric depth from a single camera by combining an AI depth-estimation model with a tracking algorithm that provides real-world scale. It contrasts this approach with LiDAR and describes the role of depth ambiguity in camera-based navigation.

### Source excerpt

Take a photo of a coffee mug on your desk. Now look at that photo...