13 citations · 48 across the 13 of their papers we have counts for
13 papers · 1 filter
SC-wLS: Towards Interpretable Feed-forward Camera Re-localization
Xin Wu, Hao Zhao, Shunkai Li +2
Visual re-localization aims to recover camera poses in a known environment, which is vital for applications like robotics or augmented reality. Feed-forward absolute camera pose re…
LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF
Zhenxin Zhu, Yuantao Chen, Zirui Wu +6
Neural Radiance Fields (NeRFs) have made great success in representing complex 3D scenes with high-resolution details and efficient memory. Nevertheless, current NeRF-based pose es…
Language-guided Semantic Style Transfer of 3D Indoor Scenes
Bu Jin, Beiwen Tian, Hao Zhao +1
We address the new problem of language-guided semantic style transfer of 3D indoor scenes. The input is a 3D indoor scene mesh and several phrases that describe the target scene. F…
Transferable End-to-end Room Layout Estimation via Implicit Encoding
Hao Zhao, Rene Ranftl, Yurong Chen +1
We study the problem of estimating room layouts from a single panorama image. Most former works have two stages: feature extraction and parametric model fitting. Here we propose an…
Semi-supervised Implicit Scene Completion from Sparse LiDAR
Pengfei Li, Yongliang Shi, Tianyu Liu +3
Recent advances show that semi-supervised implicit representation learning can be achieved through physical constraints like Eikonal equations. However, this scheme has not yet bee…
Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing
Xiaoxue Chen, Tianyu Liu, Hao Zhao +2
Multi-task indoor scene understanding is widely considered as an intriguing formulation, as the affinity of different tasks may lead to improved performance. In this paper, we tack…
Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck
Liyi Luo, Beiwen Tian, Hao Zhao +1
Semantic understanding of 3D point clouds is important for various robotics applications. Given that point-wise semantic annotation is expensive, in this paper, we address the chal…
PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds
Xiaoxue Chen, Hao Zhao, Guyue Zhou +1
3D scene understanding from point clouds plays a vital role for various robotic applications. Unfortunately, current state-of-the-art methods use separate neural networks for diffe…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…
Efficient Semantic Scene Completion Network with Spatial Group Convolution
Jiahui Zhang, Hao Zhao, Anbang Yao +3
We introduce Spatial Group Convolution (SGC) for accelerating the computation of 3D dense prediction tasks. SGC is orthogonal to group convolution, which works on spatial dimension…
Deeply-supervised Knowledge Synergy
Dawei Sun, Anbang Yao, Aojun Zhou +1
Convolutional Neural Networks (CNNs) have become deeper and more complicated compared with the pioneering AlexNet. However, current prevailing training scheme follows the previous…
A Closed-form Solution to Universal Style Transfer
Ming Lu, Hao Zhao, Anbang Yao +3
Universal style transfer tries to explicitly minimize the losses in feature space, thus it does not require training on any pre-defined styles. It usually uses different layers of…
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
Hao Zhao, Ming Lu, Anbang Yao +3
In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the benefits of two novel techniques. The first one is seman…