20 citations · 54 across the 4 of their papers we have counts for
10 papers
UniOcc: Unifying Vision-Centric 3D Occupancy Prediction with Geometric and Semantic Rendering
Mingjie Pan, Li Liu, Jiaming Liu +6
In this technical report, we present our solution, named UniOCC, for the Vision-Centric 3D occupancy prediction track in the nuScenes Open Dataset Challenge at CVPR 2023. Existing…
Global Aggregation then Local Distribution for Scene Parsing
Xiangtai Li, Li Zhang, Guangliang Cheng +4
Modelling long-range contextual relationships is critical for pixel-wise prediction tasks such as semantic segmentation. However, convolutional neural networks (CNNs) are inherentl…
Towards Efficient Scene Understanding via Squeeze Reasoning
Xiangtai Li, Xia Li, Ansheng You +5
Graph-based convolutional model such as non-local block has shown to be effective for strengthening the context modeling ability in convolutional neural networks (CNNs). However, i…
Feature-metric Loss for Self-supervised Learning of Depth and Egomotion
Chang Shu, Kun Yu, Zhixiang Duan +1
Photometric loss is widely used for self-supervised depth and egomotion estimation. However, the loss landscapes induced by photometric differences are often problematic for optimi…
Semantic Flow for Fast and Accurate Scene Parsing
Xiangtai Li, Ansheng You, Zhen Zhu +4
In this paper, we focus on designing effective method for fast and accurate scene parsing. A common practice to improve the performance is to attain high resolution feature maps wi…
Global Aggregation then Local Distribution in Fully Convolutional Networks
Xiangtai Li, Li Zhang, Ansheng You +3
It has been widely proven that modelling long-range dependencies in fully convolutional networks (FCNs) via global aggregation modules is critical for complex scene understanding t…