3D reconstruction is a longstanding ill-posed problem, which has been explored for decades
by the computer vision, computer graphics, and machine learning communities. Since 2015 …

We present an efficient 3D object detection framework based on a single RGB image in the
scenario of autonomous driving. Our efforts are put on extracting the underlying 3D …

We present a fast inverse-graphics framework for instance-level 3D scene understanding.
We train a deep convolutional network that learns to map image regions to the full 3D shape …

Deep learning methods are successfully used in applications pertaining to ubiquitous
computing, pervasive intelligence, health, and well-being. Specifically, the area of human …

This paper proposes a deep neural architecture, PlaneRCNN, that detects and reconstructs
piecewise planar regions from a single RGB image. PlaneRCNN employs a variant of Mask …

Human activity recognition (HAR) using wearable sensors is always a research hotspot in
ubiquitous computing scenario, in which feature learning has played a crucial role. Recent …

Autonomous driving has attracted remarkable attention from both industry and academia. An
important task is to estimate 3D properties (eg translation, rotation and shape) of a moving or …

Recent work has shown good recognition results in 3D object recognition using 3D
convolutional networks. In this paper, we show that the object orientation plays an important …

One core challenge in object pose estimation is to ensure accurate and robust performance
for large numbers of diverse foreground objects amidst complex background clutter. In this …

The success of deep learning is usually accompanied by the growth in neural network
depth. However, the traditional training method only supervises the neural network at its last …