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Watanabe Lab
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The lab uses AI to study the processing and recognition of video and images.
AboutAbout the lab
Advancing the processing and recognition of video and images.
The Watanabe Lab studies the processing and recognition of video and images.
It works on a wide range of topics, including video coding using deep learning, point clouds, and omnidirectional images.
The lab also puts particular effort into research on technology that compresses video in a form that computers can easily understand (Video Coding for Machines).
Key terms
- Deep learning
- A computational approach modeled on the connections between neurons in the brain, which learns features from many examples
- Point cloud
- Data that represents the shape of an object as a collection of many points
- Diffusion model
- An AI technique that creates a desired image by gradually removing noise
NewsLab news
Student presentation received the Presentation Award at IEEE ICCE-TW 2026
Paper received the IEICE Paper Award
Student received the Oral Presentation Award at IEEE GCCE 2025
Master's student received the IEICE Academic Encouragement Award
Student received the Presentation Award at IEEE GCCE 2024
ResearchResearch themes
The Watanabe Lab studies the processing and recognition of video and images. It works on a wide range of topics, including video coding using deep learning, point clouds, and omnidirectional images.
*Illustrative imageAI-based video frame interpolation
Using diffusion models, an AI technique, the lab studies artificially generating images to fill in the gaps between video frames.
*Illustrative imageObject detection and tracking in video
In Video Coding for Machines, a technology that encodes video in a form that computers can easily understand, the lab studies object detection within frames and object tracking across frames.
*Illustrative imageDistance estimation from omnidirectional images
The lab studies estimating the distance to objects in a scene using images from a stereo camera that can capture all directions.
*Illustrative imageMaking point cloud data lightweight
The lab studies using convolutional neural networks to thin out point clouds, which represent the shape of an object as a collection of points, and to extract features from them. It also works on mesh simplification to reduce the size of point cloud data.
MembersFaculty
The Watanabe Lab consists of faculty members, invited researchers, and master's and undergraduate students.As of 2026
- ProfessorHiroshi Watanabe
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Ouster OS1-64 lidar point cloud of intersection of Folsom and Dore St, San Francisco” Daniel L. Lu / CC BY 4.0 (cropped)
- AI-based video frame interpolation: “Moving Water and Ink (Unsplash)” Adrien Ledoux / CC0 (cropped)
- Object detection and tracking in video: “Footprints in the snow, Munich 2021 06” Kritzolina / CC BY-SA 4.0 (cropped)
- Distance estimation from omnidirectional images: “Dragonfly eyes closeup” Вых Пыхманн / CC BY-SA 3.0 (cropped)
- Making point cloud data lightweight: “Pflanze-Sonnenblume1-Asio (cropped)” Chiswick Chap / CC BY-SA 3.0 (cropped)
- Related lab (Kobayashi Lab): “Spectrogram of Papuan Malay sentence de bicara keras” Emflazie / CC0 (cropped)
- Related lab (Ogawa Lab): “Spectrogram - It Rains a Lot in Portland” Aaron Parecki / CC BY 2.0 (cropped)
- Related lab (Kasai Lab): “Koch V3 85 Grad K HQ OpenCL” PantheraLeo1359531 / CC BY 4.0 (cropped)
- Related lab (Katto Lab): “Studio A broadcast area ( )” Atomic Taco from Seattle, WA, USA / CC BY-SA 2.0 (cropped)
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