3D object detection is vital for many robotics applications. For tasks where
a 2D perspective range image exists, we propose to learn a 3D representation
directly from this range image view. To this end, we designed a 2D
convolutional network architecture that carries the 3D spherical coordinates of
each pixel throughout the network. Its layers can consume any arbitrary
convolution kernel in place of the default inner product kernel and exploit the
underlying local geometry around each pixel. We outline four such kernels: a
dense kernel according to the bag-of-words paradigm, and three graph kernels
inspired by recent graph neural network advances: the Transformer, the
PointNet, and the Edge Convolution. We also explore cross-modality fusion with
the camera image, facilitated by operating in the perspective range image view.
Our method performs competitively on the Waymo Open Dataset and improves the
state-of-the-art AP for pedestrian detection from 69.7% to 75.5%. It is also
efficient in that our smallest model, which still outperforms the popular
PointPillars in quality, requires 180 times fewer FLOPS and model parameters

该研究提出了一种基于 2D 透视范围图像的卷积神经网络架构方法，可用于直接从范围图像视图学习 3D 表示。通过利用本地几何和跨模态融合处理，该方法在行人检测方面的 AP 从 69.7％提高到 75.5％，同时模型参数和操作速度均表现良好。

基于图卷积核的三维点云物体检测

To the Point: Efficient 3D Object Detection in the Range Image with  Graph Convolution Kernels

We present Hand-CNN, a novel convolutional network architecture for detecting
hand masks and predicting hand orientations in unconstrained images. Hand-CNN
extends MaskRCNN with a novel attention mechanism to incorporate contextual
cues in the detection process. This attention mechanism can be implemented as
an efficient network module that captures non-local dependencies between
features. This network module can be inserted at different stages of an object
detection network, and the entire detector can be trained end-to-end.
We also introduce a large-scale annotated hand dataset containing hands in
unconstrained images for training and evaluation. We show that Hand-CNN
outperforms existing methods on several datasets, including our hand detection
benchmark and the publicly available PASCAL VOC human layout challenge. We also
conduct ablation studies on hand detection to show the effectiveness of the
proposed contextual attention module.

该研究介绍了 Hand-CNN 这个新的卷积神经网络架构，用于在不受限条件下检测手的掩模和预测手的定向。此外，该研究还介绍了一个包含手部在限制松散条件下的图像的大型手部注释数据集。最后，该研究证明了 Hand-CNN 在多个数据集上的优越表现，同时也进行了手部检测的消融研究以证明所提出的上下文注意力机制的有效性。

野外手部检测的上下文关注

Contextual Attention for Hand Detection in the Wild

In this work, we propose a novel and efficient method for articulated human
pose estimation in videos using a convolutional network architecture, which
incorporates both color and motion features. We propose a new human body pose
dataset, FLIC-motion, that extends the FLIC dataset with additional motion
features. We apply our architecture to this dataset and report significantly
better performance than current state-of-the-art pose detection systems.

本研究提出了一种新颖高效的卷积神经网络结构，其中融合了颜色和运动特征，可用于视频中的人类姿态估计。同时，作者还创建了包含运动特征的新数据集 FLIC-motion，并对其应用了该网络结构，结果表明其性能显著优于当前最先进的姿态检测系统。