Seeing the Unseen：Data-Driven 3D Scene Understanding
報(bào)告專家:宋舒然 普林斯頓大學(xué)博士，將任職哥倫比亞大學(xué)助理教授
報(bào)告時(shí)間:2018年12月9日星期日，10:00~11:00
報(bào)告地點(diǎn):850 會(huì)議室
報(bào)告摘要：
Intelligent robots require advanced vision capabilities to perceive and interact with the real physical world. While computer vision has made great strides in recent years, its predominant paradigm still focuses on analyzing image pixels to infer 2D output representations (bounding boxes, segmentations, etc.), which remain far from sufficient for real-world robotics applications.
In this talk, I will advocate the use of complete 3D scene representations that enable intelligent systems to not only recognize what is seen (e.g. Am I looking at a chair?), but also predict contextual information about the complete 3D environment beyond visible surfaces (e.g. What could be behind the table? Where should I look to find an exit?). As examples, I will present a line of my recent works that demonstrate the power of these representations through amodal 3D object detection (Sliding Shape and Deep Sliding Shapes), analyzing and synthesizing 3D scenes (Semantic Scene Completion), and predicting semantic and 3D structure outside the image field of view (Im2Pano3D). Finally, I will discuss some ongoing efforts on how these 3D scene representations can further enable and benefit from real-world robotic interactions (in the Amazon Robotic Challenge), shifting the way we view computer vision problems from the perspective of a passive observer to that of an active explorer.
專家簡(jiǎn)介：
宋舒然于2018年從美國(guó)普林斯頓大學(xué)畢業(yè)獲得博士學(xué)位（導(dǎo)師：Thomas Funkhouser），將于2019年加入美國(guó)哥倫比亞大學(xué)擔(dān)任助理教授，目前在谷歌公司擔(dān)任訪問研究員。研究方向?yàn)椋河?jì)算機(jī)圖形學(xué)、計(jì)算機(jī)視覺、機(jī)器人等。宋舒然本科畢業(yè)于香港科技大學(xué)，博士期間曾獲 Facebook 獎(jiǎng)學(xué)金， 西貝爾獎(jiǎng)學(xué)金等。研究主頁(yè)：http://vision.princeton.edu/people/shurans/