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4月2日晚7:30-9:00

AI TIME特别邀请了两位优秀的讲者跟大家共同开启浙大CAD&CG实验室专场二！

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观看直播

链接：https://live.bilibili.com/21813994

★ 邀请嘉宾 ★

黄昭阳：现为香港中文大学MMLab一年级博士生（导师：李鸿升），2019年硕士毕业于浙江大学CAD&CG国家重点实验室（导师：章国锋）。相关研究成果发表在顶级会议与期刊ICCV, CVPR, TVCG上。

报告题目：

基于像素投票与图像分割的视觉定位

摘要：

Visual localization is of great importance in robotics and computer vision. Recently, scene coordinate regression based methods have shown good performance in visual localization in small static scenes. However, it still estimates camera poses from many inferior scene coordinates. To address this problem, we propose a novel visual localization framework that establishes 2D-to-3D correspondences between the query image and the 3D map with a series of learnable scene-specific landmarks. In the landmark generation stage, the 3D surfaces of the target scene are over-segmented into mosaic patches whose centers are regarded as the scene-specific landmarks. To robustly and accurately recover the scene-specific landmarks, we propose the  Voting with Segmentation Network (VS-Net) to segment the pixels into different landmark patches with a segmentation branch and estimate the landmark locations within each patch with a landmark location voting branch. Since the number of landmarks in a scene may reach up to 5000, training a segmentation network with such a large number of classes is both computation and memory costly for the commonly used cross-entropy loss. We propose a novel prototype-based triplet loss with hard negative mining, which is able to train semantic segmentation networks with a large number of labels efficiently. Our proposed VS-Net is extensively tested on multiple public benchmarks and can outperform state-of-the-art visual localization methods.

论文标题：

VS-Net: Voting with Segmentation for Visual Localization 

论文链接：

http://www.cad.zju.edu.cn/home/xzhou/publication/cvpr2021vs/

谢一鸣：浙江大学CAD&CG国家重点实验室科研助理（导师：周晓巍）。主要研究方向为三维场景重建与三维物体检测。相关研究成果在CVPR等学术会议上发表。本科毕业于浙江大学。

报告题目：

基于单目视频的三维场景实时重建

摘要：

We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video. Unlike previous methods that estimate single-view depth maps separately on each key-frame and fuse them later, we propose to directly reconstruct local surfaces represented as sparse TSDF volumes for each video fragment sequentially by a neural network. A learning-based TSDF fusion module based on gated recurrent units is used to guide the network to fuse features from previous fragments. This design allows the network to capture local smoothness prior and global shape prior of 3D surfaces when sequentially reconstructing the surfaces, resulting in accurate, coherent, and real-time surface reconstruction. The experiments on ScanNet and 7-Scenes datasets show that our system outperforms state-of-the-art methods in terms of both accuracy and speed. To our best knowledge, this is the first learning-based system that is able to reconstruct dense coherent 3D geometry in real-time.

论文标题：

NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video

论文链接：

https://zju3dv.github.io/neuralrecon‘

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