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Okutomi-Monno Lab
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An image processing lab that reads three-dimensional shapes and motion from camera images.
AboutAbout the lab
Reading shape and motion from camera images
The Okutomi-Monno Lab studies how to process images captured by cameras and sensors to extract information such as 3D shape and motion.
The lab handles a wide variety of images, including street scenes captured by vehicle-mounted cameras, medical images from endoscopes and MRI, and images from polarization cameras.
The lab also works on image restoration and recognition using deep learning.
Although the lab's website is separate from the Tanaka Lab's, the two labs continue to be run in cooperation as before.
Key terms
- Polarization
- A property of light whose waves oscillate in a uniform direction. A polarization camera records the light intensity for each oscillation direction
- Demosaicing
- Processing that fills in missing information to complete an image from a sensor that records only some of the colors or polarization at each pixel
- Point cloud
- Data that represents the surface of an object as a collection of many points with 3D positions
NewsLab news
Students and faculty received the Best Academic Award at the Image Sensing Symposium
Students and faculty received the Excellent Academic Award at the Image Sensing Symposium
Students and faculty received the Audience Award at the Image Sensing Symposium
Released training data and an AI benchmark for rheumatic disease image analysis, together with Hokkaido University and others
A student received the Systems Division Outstanding Presentation Award at Institute of Science Tokyo
ResearchResearch themes
The lab studies techniques for estimating 3D shape and the motion of objects from images, as well as techniques for cleaning up images degraded by blur, noise, rain, and similar effects. Its research introduction page lists many themes, including street-scene images from vehicle-mounted cameras, support for image-based diagnosis of rheumatoid arthritis, and 3D reconstruction from endoscopic images.
*Illustrative imageView synthesis from vehicle-mounted camera images
This research uses street-scene images captured by cameras mounted on a vehicle to generate images of the scene from new viewpoints that were not actually captured.
*Illustrative imageDemosaicing for polarization cameras
For monochrome and color polarization cameras that capture polarization information in a single shot, the lab develops demosaicing methods that produce high-quality images, using both interpolation-based and learning-based approaches.
*Illustrative image2D and 3D segmentation of street-scene images
Toward large-scale urban mapping, the lab segments regions such as buildings from unordered street-scene images and links them to 3D point clouds, so that the same object can be tracked even when the viewpoint changes substantially.
*Illustrative imageObject tracking with multiple cameras
This research tracks people and objects in video from multiple cameras with overlapping fields of view. By combining the positional relationships between cameras with similarity in appearance, the lab enables the system to keep tracking the same person even when that person is hidden from one camera.
MembersFaculty
The Okutomi-Monno Lab consists of faculty members, secretaries, and doctoral, master's, and undergraduate students.As of 2026
- Project ProfessorMasatoshi OkutomiPublications ↗
- Project Associate ProfessorYusuke MonnoPublications ↗
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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: “Monmouth castle point cloud, created with Photosynth 01” John Cummings / CC BY-SA 3.0 (cropped)
- View synthesis from vehicle-mounted camera images: “Google Street View camera car in Barcelona” Jove / CC0 (cropped)
- Demosaicing for polarization cameras: “BiodegradablePlasticUtensils1” Scott Bauer / Public domain (cropped)
- 2D and 3D segmentation of street-scene images: “Ouster OS1-64 lidar point cloud of intersection of Folsom and Dore St, San Francisco” Daniel L. Lu / CC BY 4.0 (cropped)
- Object tracking with multiple cameras: “Pole with surveillance cameras in Saarbrücken” µKöff / CC BY 4.0 (cropped)
- Related lab (Tanaka Lab): “Пиксели матрицы видеорегистратора” Gribkov / CC BY 4.0 (cropped)
- Related lab (Kawakami Lab): “PIA16239 High-Resolution Self-Portrait by Curiosity Rover Arm Camera” NASA/JPL-Caltech/Malin Space Science Systems Derivative work including grading, distortion correction, minor local adjustments and rendering from tiff-file: Julian Herzog / Public domain (cropped)
- Related lab (Hara Lab): “Scratch on glass surface, 3D topography ( )” ZEISS Microscopy from Germany / CC BY-SA 2.0 (cropped)
- Related lab (Amaya Lab): “Electrode protecting a screw” Rémi Kaupp / CC BY-SA 3.0 (cropped)
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