Complex-YOLO: Real-time 3D Object Detection on Point Clouds

Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and motion planning. The capacity of inferencing highly sparse 3D data in real-time is an ill-posed problem for lots of other application areas besides automated vehicles, e.g. augmented reality, personal robotics or industrial automation... (read more)

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Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
Global Average Pooling
Pooling Operations
1x1 Convolution
Convolutions
Batch Normalization
Normalization
Max Pooling
Pooling Operations
Softmax
Output Functions
Convolution
Convolutions
Darknet-19
Convolutional Neural Networks
YOLOv2
Object Detection Models