Discovery of Latent 3D Keypoints and Imposition of Prior in Deep Learning

วันที่ 4 กุมภาพันธ์ 2562 (18.00น. – 21.00น.)

สถานที่ อาคารณ์อนุสรณ์ 50 ปี

We are back for our first meetup in 2019 ! This time we are pleased to have two speakers from VISTEC, Dr. Supasorn Suwajanakorn and Dr. Nat Dilokthanakul. Dr. Supasorn will present his NIPS paper "Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning" and Dr. Nat will give an introduction about the imposition of prior knowledge in Deep Learning.

Agenda:

6:00 PM - 6:30 PM | Registration

6:30PM - 7:30 PM | Dr. Supasorn Suwajanakorn, Lecturer at VISTEC
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning

7:30 PM - 8:30 PM | Dr. Nat Dilokthanakul, Postdoctoral Researcher at VISTEC
Imposition of prior knowledge in Deep Learning

8:30 PM - 9:00 PM | Networking

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Topic 1 : Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Speaker: Dr. Supasorn Suwajanakorn

Abstract of the talk:
This paper presents KeypointNet, an end-to-end geometric reasoning framework to learn an optimal set of category-specific 3D keypoints, along with their detectors.
Given a single image, KeypointNet extracts 3D keypoints that are optimized for a downstream task. We demonstrate this framework on 3D pose estimation by proposing a differentiable objective that seeks the optimal set of keypoints for recovering the relative pose between two views of an object. Our model discovers geometrically and semantically consistent keypoints across viewing angles and instances of an object category. Importantly, we find that our end-to-end framework using no ground-truth keypoint annotations outperforms a fully supervised baseline using the same neural network architecture on the task of pose estimation. The discovered 3D keypoints on the car, chair, and plane categories of ShapeNet [6] are visualized at keypointnet.github.io