Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Understanding behavior requires datasets that capture humans while carrying out complex tasks. The kitchen is an excellent environment for assessing human motor and cognitive function, as many complex actions are naturally exhibited in kitchens from choppi ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

We propose a test -time adaptation for 6D object pose tracking that learns to adapt a pre -trained model to track the 6D pose of novel objects. We consider the problem of 6D object pose tracking as a 3D keypoint detection and matching task and present a mo ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Light and sound waves can move objects through the transfer of linear or angular momentum, which has led to the development of optical and acoustic tweezers, with applications ranging from biomedical engineering to quantum optics. Although impressive manip ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-de ...

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

Over the years, clinical institutes accumulated large amounts of digital slides from resected tissue specimens. These digital images, called whole slide images (WSIs), are high-resolution tissue snapshots that depict the complex interaction of cells at the ...

An electroadhesive gripping system and a method for gripping and manipulating an object are disclosed. The invention provides a novel approach particularly useful to pick fabrics or otherwise flat and flexible objects including a method relying on electroa ...

The monumental progress in the development of machine learning models has led to a plethora of applications with transformative effects in engineering and science. This has also turned the attention of the research community towards the pursuit of construc ...

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

Background and Objective: Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with contagious diseases, many research teams have turned ...

We propose a method to extract orthogonal views of facades from photogrammetric models of cities. This method was applied to extract all facades of the city of Venice. The result images open up new areas of research in architectural history. ...

The study of non-contact manipulation in water, and the ability to robotically control floating objects has gained recent attention due to wide-ranging potential applications, including the analysis of plastic pollution in the oceans and the optimization o ...

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-av ...

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Chat with Graph Search

EPFL-Smart-Kitchen-30 Annotations and Poses

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Test-time adaptation for 6D pose tracking

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Wave-momentum shaping for moving objects in heterogeneous and dynamic media

Robust machine learning for neuroscientific inference

Topics in statistical physics of high-dimensional machine learning

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Unlabeled Principal Component Analysis and Matrix Completion

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Self-Supervised Learning for Patient Stratification and Survival Analysis in Computational Pathology: An Application to Colorectal Cancer

Electroadhesive gripping system and method for gripping an object

Probabilistic methods for neural combinatorial optimization

Equivariant Neural Architectures for Representing and Generating Graphs

Transfer learning application of self-supervised learning in ARPES

A semi-supervised algorithm for improving the consistency of crowdsourced datasets: The COVID-19 case study on respiratory disorder classification

The Skin of Venice: Automatic Facade Extraction from Point Clouds

Non-contact robotic manipulation of floating objects: exploiting emergent limit cycles

Using machine learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan region