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Frank Michel

7 accepted papers

2023

Demonstrating Mobile Manipulation in the Wild: A Metrics-Driven Approach

RSS 2023poster

We present our general-purpose mobile manipulation system consisting of a custom robot platform and key algorithms spanning perception and planning. To extensively test the system in the wild and benchmark its performance, we choose a grocery shopping scenario in an actual, unmodified grocery store.…

2018

BOP: Benchmark for 6D Object Pose Estimation

ECCV 2018poster

We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets in a unified format that cover different practical…

2017

DSAC - Differentiable RANSAC for Camera Localization

CVPR 2017oral

RANSAC is an important algorithm in robust optimization and a central building block for many computer vision applications. In recent years, traditionally hand-crafted pipelines have been replaced by deep learning pipelines, which can be trained in an end-to-end fashion. However, RANSAC has so far n…

Cited by 737PDFcodeScholar
2017

Global Hypothesis Generation for 6D Object Pose Estimation

CVPR 2017spotlight

This paper addresses the task of estimating the 6D-pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) compute local features; ii) generate a pool of pose-hypotheses; iii) select and refine a pose from the pool. This work focuses on the seco…

Cited by 156PDFScholar
2017

PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning

CVPR 2017poster

State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational resources to promising candidates, however, such decisions are non-differentiable. As a result, these algorithms are hard t…

Cited by 60PDFScholar
2016

Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image

CVPR 2016poster

In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we…

Cited by 627PDFScholar
2015

Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images

ICCV 2015poster

Analysis-by-synthesis has been a successful approach for many tasks in computer vision, such as 6D pose estimation of an object in an RGB-D image which is the topic of this work. The idea is to compare the observation with the output of a forward process, such as a rendered image of the object of in…

Cited by 263PDFScholar