Research And Projects

We develop computer vision methods, including 3D object detection, hand pose estimation, and geo-localization, with application to augmented reality and robotics.


Segmentation-Based 3D Tracking

Given simple 2.5D city maps, we show how to exploit recent results in semantic segmentation to efficiently track a camera in urban environments.

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3D Pose Estimation

BB8 is a novel method for 3D object detection and pose estimation from color images only. It predicts the 3D poses of the objects in the form of 2D projections of the 8 corners of their 3D bounding boxes.

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ALCN: Adaptive Local Contrast Normalization

We propose a novel illumination normalization method that lets us learn to detect objects and estimate their 3D poses under challenging illumination condition from very few training samples.

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Geo-localization from Images and 2.5D Maps

We propose methods for accurate camera pose estimation in urban environments from single images and 2.5D maps made of the surrounding buildings’ outlines and their heights.

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Accurate Geo-Localization from Images

We present a method for large-scale geo-localization and global tracking of mobile devices in urban outdoor environments.

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Hand Detection and 3D Pose Estimation

We introduce a prior model for predicting the 3D joint locations of a hand given a depth map using Convolutional Neural Networks (CNN).

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Object Detection and 3D Pose Estimation

We introduce a simple but powerful approach to computing descriptors for object views that efficiently capture both the object identity and 3D pose.

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3D Object Tracking

We present a method that estimates in real-time and under challenging conditions the 3D pose of a known object.

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Learning to Detect Keypoints (at CVLab, EPFL)

We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisingly sensitive.

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Flying Object Detection from a Single Moving Camera (at CVLab, EPFL)

We propose an approach to detect flying objects such as UAVs and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves.

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Realistic Synthetic Data Generation (at CVLab, EPFL)

We propose a novel approach to synthesizing images that are effective for training object detectors.

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Older Projects at CVLab

CVLab

Team Lepetit
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