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CUDA-PointPillars

A project demonstrating how to use CUDA-PointPillars to deal with cloud points data from lidar.

Model DevelopmentArchitecturePython
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创建于 2021-11-16 · 更新于 2026-10-02 · 今日第 15366 名
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NVIDIA-AI-IOT/CUDA-PointPillars 排名第 15,366，有 659 个星标。

同类别比较

NVIDIA-AI-IOT/CUDA-PointPillars 排名第 15,366，此处有 8 个同类条目；MorvanZhou/pytorch-A3C 排名第 15,365。

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关键参数与规格
关键参数与规格
参数值
语言Python
类别Model Development
子类别Architecture
关键词3D, Autonomous vehicles, Object detection
星标数659
分叉数185
贡献者2
创建日期2021-11-16
更新日期2026-10-02
1 天星标数0
7 天星标数0
排名15,366
上次排名15,180
排名变化-186
7 天星标排名14,958
上次 7 天星标排名14,963
7 天星标排名变化5
总星标排名15,579
上次总星标排名15,552
总星标排名变化-27
国家USA
README 摘录

PointPillars Inference with TensorRT This repository contains sources and model for pointpillars inference using TensorRT. Overall inference has below phases: Voxelize points cloud into 10-channel features Run TensorRT engine to get detection feature Parse detection feature and apply NMS Prerequisites Prepare Model && Data We provide a Dockerfile to ease environment setup. Please execute the following command to build the docker image after nvidia-docker installation: We can then run the docker with the following command: For model exporting, please

run the following command to clone pcdet repo and install custom CUDA extensions: Download PTM to ckpts/, then use below command to export ONNX model: Use below command to evaluate on kitti dataset, follow Evaluation on Kitti to get more detail for dataset preparation. Setup Runtime Environment Nvidia Jetson Orin + CUDA 11. 11 Compile && Run FP16 Performance && Metrics Average perf in FP16 on the training set(7481 instances) of KITTI dataset. 3D moderate metrics on the validation set(3769 instances

) of KITTI dataset. Note Voxelization has random output since GPU processes all points simultaneously while points selection for a voxel is random. References Detecting Objects in Point Clouds with NVIDIA CUDA-Pointpillars PointPillars: Fast Encoders for Object Detection from Point Clouds

常见问题

记录了多少星标和分叉？

NVIDIA-AI-IOT/CUDA-PointPillars 有 659 个星标和 185 个分叉。

仓库排名是多少？

NVIDIA-AI-IOT/CUDA-PointPillars 排名第 15,366。

记录的语言和类别是什么？

NVIDIA-AI-IOT/CUDA-PointPillars 有 659 个星标，语言为 Python，类别为 Model Development。

哪个仓库排名最接近？

NVIDIA-AI-IOT/CUDA-PointPillars 与 MorvanZhou/pytorch-A3C 比较，后者排名第 15,365，有 659 个星标。

README

PointPillars Inference with TensorRT

This repository contains sources and model for pointpillars inference using TensorRT.

Overall inference has below phases:

  • Voxelize points cloud into 10-channel features
  • Run TensorRT engine to get detection feature
  • Parse detection feature and apply NMS

Prerequisites

Prepare Model && Data

We provide a Dockerfile to ease environment setup. Please execute the following command to build the docker image after nvidia-docker installation:

cd docker && docker build . -t pointpillar

We can then run the docker with the following command:

nvidia-docker run --rm -ti -v /home/$USER/:/home/$USER/ --net=host --rm pointpillar:latest

For model exporting, please run the following command to clone pcdet repo and install custom CUDA extensions:

git clone https://github.com/open-mmlab/OpenPCDet.git
cd OpenPCDet && git checkout 846cf3e && python3 setup.py develop

Download PTM to ckpts/, then use below command to export ONNX model:

python3 tool/export_onnx.py --ckpt ckpts/pointpillar_7728.pth --out_dir model

Use below command to evaluate on kitti dataset, follow Evaluation on Kitti to get more detail for dataset preparation.

sh tool/evaluate_kitti_val.sh

Setup Runtime Environment

  • Nvidia Jetson Orin + CUDA 11.4 + cuDNN 8.9.0 + TensorRT 8.6.11

Compile && Run

sudo apt-get install git-lfs && git lfs install
git clone https://github.com/NVIDIA-AI-IOT/CUDA-PointPillars.git
cd CUDA-PointPillars && . tool/environment.sh
mkdir build && cd build
cmake .. && make -j$(nproc)
cd ../ && sh tool/build_trt_engine.sh
cd build && ./pointpillar ../data/ ../data/ --timer

FP16 Performance && Metrics

Average perf in FP16 on the training set(7481 instances) of KITTI dataset.

| Function(unit:ms) | Orin   |
| ----------------- | ------ |
| Voxelization      | 0.18   |
| Backbone & Head   | 4.87   |
| Decoder & NMS     | 1.79   |
| Overall           | 6.84   |

3D moderate metrics on the validation set(3769 instances) of KITTI dataset.

|                   | Car@R11 | Pedestrian@R11 | Cyclist@R11  | 
| ----------------- | --------| -------------- | ------------ |
| CUDA-PointPillars | 77.00   | 52.50          | 62.26        |
| OpenPCDet         | 77.28   | 52.29          | 62.68        |

Note

  • Voxelization has random output since GPU processes all points simultaneously while points selection for a voxel is random.

References

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