Deep OD SEG 3D - jungwonkang/references GitHub Wiki
- Multi-view 3D object detection network for autonomous driving (MV3D) (CVPR 2017)
- AVOD: Joint 3D proposal generation and object detection from view aggregation (2018)
- Fusing bird view LIDAR point cloud and front view camera image for deep object detection (2018)
- SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
- SqueezeSeg: convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3d lidar point cloud (2017)
- HDNET: Exploiting HD Maps for 3D Object Detection
- PIXOR: Real-time 3D Object Detection from Point Clouds
- Vote3deep: fast object detection in 3d point clouds using efficient convolutional neural networks
- Complex-YOLO: Real-time 3D Object Detection on Point Clouds
- PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud
- Efficient convolutions for real-time semantic segmentation of 3D point clouds (3DV 2018)
- Deep continuous fusion for multi-sensor 3D object detection (ECCV 2018)
- SECOND: Sparsely embedded convolutional detection
- PointPillars: Fast encoders for object detection from point clouds
- 3D object detection for autonomous driving using deep learning
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Semantic3D.net: A new large-scale point cloud classification benchmark (2017)
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Large-scale point cloud segmentation with superpoint graphs (CVPR 2018)
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SnapNet: Unstructured point cloud semantic labeling using deep segmentation networks (2017)
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PointNet: Deep learning on point sets for 3D classification and segmentation (CVPR 2017)
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PointNet++: Deep hierarchical feature learning on point sets in a metric space (NIPS 2017)
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Efficient convolutions for real-time semantic segmentation of 3D point clouds
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3D recurrent neural networks with context fusion for point cloud semantic segmentation (ECCV 2018)