Shengheng Deng's research while affiliated with South China University of Technology and other places
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Publications (5)
3D object detection has recently received much attention due to its great potential in autonomous vehicle (AV). The success of deep learning based object detectors relies on the availability of large-scale annotated datasets, which is time-consuming and expensive to compile, especially for 3D bounding box annotation. In this work, we investigate di...
Detecting objects from LiDAR point clouds is of tremendous significance in autonomous driving. In spite of good progress, accurate and reliable 3D detection is yet to be achieved due to the sparsity and irregularity of LiDAR point clouds. Among existing strategies, multi-view methods have shown great promise by leveraging the more comprehensive inf...
The ability to understand the ways to interact with objects from visual cues, a.k.a. visual affordance, is essential to vision-guided robotic research. This involves categorizing, segmenting and reasoning of visual affordance. Relevant studies in 2D and 2.5D image domains have been made previously, however, a truly functional understanding of objec...
Citations
... Attention mechanisms [32,33] have been applied to vision tasks such as target detection and image classification. To focus more on features that are important in space and channel locations, Wang et al. [34] use a residual attention mechanism to enhance key features of an image. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/1125586277797888-1645372089062_Q64/Zhihao-Liang-3.jpg)
... Besides, affordance detection in 3D data also has been explored. Deng et al. [34] propose a 3D point cloud affordance detection dataset based on PartNet [35] and ShapeNet [36]. Xu et al. [37] propose part-level affordance detection from 3D Objects. ...
