Gaussian based

绷不住了，又有人刷榜了

在这项工作中，Hu Cao 等人建立了一个高效且鲁棒的全卷积神经网络，从来自真实场景的 n 通道图像中获取抓取信息。

lightweight generative architecture
a grasping representation based on Gaussian kernel
a Receptive Field Block is assembled to the bottleneck of grasping detection architecture
Combined pixel attention and channel attention

Grasping is a challenge task for robots: perception, planning and extection.

作者贡献如下：

We propose a Gaussian-based grasping representation, which relects the maximum grasping score at the center point location and can signigicantly improve the grasping detection accuracy.
We develope a lightweight generative architecture which achieves high detection accuracy and real-time running speed with small network parameters.
A receptive field block module is embedded in the bottleneck of the network to enhance its feature discriminability and robustness, and a multi-dimensional attention fusion network is developed to suppress redundant features and enhance target features in the fusion process.
Evaluation on the public Cornell and Jacquard grasping datasets demonstrate that the proposed generative based grasping detection algorithm achieves state-of-the-art performance of both speed and detection accuracy

Analytic methods: use mathematical and physical models in geometry, motion and dynamics to carry out the calculation for grasping
Empirical methods: deep learning
Classification-based: Proposals, GQ-CNN, Spatial Transformer Network
Regression-based: Multi-model fusion, ROI => more inclined to learn the mean value of the ground truth grasps
Vision and tactic sensing are fuse

GGCNN

Orientation Attentive Grasping Detection

Gaussian-based¶

\[ G_{K}=\left\{\Phi, W, Q_{K}\right\} \in \mathbb{R}^{3 \times W \times H} \]

where,

\[ Q_{K}=K(x, y)=\exp \left(-\frac{\left(x-x_{0}\right)^{2}}{2 \sigma_{x}^{2}}-\frac{\left(y-y_{0}\right)^{2}}{2 \sigma_{y}^{2}}\right) \]

where,

\[ \sigma_{x}=T_{x}, \sigma_{y}=T_{y} \]

What about classification? In some of the implementations they also output class probabilities.

What about implementation?

Are you conducting related researches?

Multiple objects?