NVIDIA-AI-IOT/Lidar_AI_Solution

Health v2 · maintenance only

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Adoption not part of the score

1867 stars · 338 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

NVIDIA's collection of GPU-accelerated Lidar AI inference solutions for autonomous driving, including optimized implementations of PointPillars, CenterPoint, and BEVFusion networks built on CUDA and TensorRT. It also ships supporting libraries such as cuPCL, 3D SparseConvolution, YUV2RGB, and cuOSD for point cloud processing and visualization.

Use cases

• run lidar 3d object detection on gpu with pointpillars
• deploy bevfusion camera-lidar fusion inference with tensorrt
• accelerate centerpoint inference with cuda and int8 quantization
• export 3d sparse convolution backbone from onnx for fast inference
• voxelize lidar point clouds with cuda kernels
• convert yuv camera frames to rgb on gpu for sensor fusion pipelines
• draw bounding boxes on lidar point cloud visualizations with cuosd
• quantize mmdet3d spconv models for edge deployment

When to choose

• you need production-grade, GPU-accelerated inference for lidar-based 3D detection networks like PointPillars, CenterPoint, or BEVFusion
• you are deploying perception pipelines on NVIDIA GPUs or Jetson edge devices and want TensorRT/CUDA optimization
• you need low-latency voxelization, NMS, or preprocessing kernels implemented in CUDA
• you want quantization (PTQ/QAT) workflows for sparse convolution lidar backbones

When to avoid

• you need a general-purpose deep learning framework rather than lidar-specific inference solutions
• your hardware is not NVIDIA CUDA-capable
• you want training-first tooling with minimal inference focus (though some training support exists, the project centers on deployment)
• you need a plug-and-play solution without C++/CUDA integration effort

Facets

library · maturity active

machine-learning deep-learning llm-inference computer-vision image-processing gpu-computing sdk developer-tools autonomous-vehicles deep-learning computer-vision gpu-computing robotics artificial-intelligence cpp lidar point-clouds 3d-object-detection bevfusion centerpoint pointpillars sparse-convolution tensorrt onnx cuda-kernels self-driving sensor-fusion int8-quantization fp16 linux cuda gpu nvidia-jetson

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markdown · JSON · MCP: product_card(name="NVIDIA-AI-IOT/Lidar_AI_Solution")