Intro to 3D Computer Vision: Business Applications and Challenges - Online Talk

We live in a world that spans 3 dimensions, but most cameras and sensors represent real-world objects using 2-dimensional images and videos. How can we recover the 3D world back from these images? One of the solutions is a 3D computer vision that utilizes deep learning for process 3D data.

For this event, we are happy to welcome Deniz Beker from the Japan’s AI figurehead Preferred Networks who will discuss the importance of 3D computer vision, its classical problems and business applications, along with different 3D representations of visual data. During his 9 years career, Deniz worked in various companies and projects and has the expertise on 2D / 3D computer vision and differentiable rendering.

📎 Key points:

• What is 3D computer vision? What are the challenges?
• What is differentiable rendering(DR)? What are the challenges?
• How deep learning is used in autonomous driving and what are the challenges?
• Application “Monocular Differentiable Rendering for Self-Supervised 3D Object Detection”
• Other examples of recent trends in academia about DR & 3D computer vision
• Open Discussion

📎 Pre-requisites:
Basic knowledge about how deep learning works is preferred but not required

🚀 About Deniz Beker 🚀

Deniz Beker obtained his Master’s degree in Electrical & Electronics from Yeditepe University, Turkey and moved to Tokyo in 2014. During his 9 years career, he worked in various companies and projects including telecommunication, face recognition, biomedical image analysis, autonomous driving perception. He has the expertise on 2D / 3D computer vision and differentiable rendering. He started working at Preferred Networks (PFN) as Engineer in Jan 2018. Besides his engineering efforts, he has also served as Engineering Manager since Dec 2019 and conducted several research projects to meet 3D computer vision with autonomous driving perception.

🚀About Le Wagon Tokyo 🚀

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