Point cloud is an important type of geometric data structure.
#2 best model for Scene Segmentation on ScanNet
Finally, we gather up the decoder layers with equivalent scales (sizes) to develop a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels.
#10 best model for Object Detection on COCO
Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications.
Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4. 4% higher accuracy with 6x fewer FLOPs.
We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the \overfeat network which was trained to perform object classification on ILSVRC13.
Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations.
SOTA for 3D Object Recognition on ModelNet40
When working with three-dimensional data, choice of representation is key.
We learn rich natural sound representations by capitalizing on large amounts of unlabeled sound data collected in the wild.