Object Pose Estimation Using Implicit Representation For Transparent Objects

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F25%3A00378611" target="_blank" >RIV/68407700:21730/25:00378611 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-91569-7_15" target="_blank" >https://doi.org/10.1007/978-3-031-91569-7_15</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-91569-7_15" target="_blank" >10.1007/978-3-031-91569-7_15</a>

Result language

angličtina

Original language name

Object Pose Estimation Using Implicit Representation For Transparent Objects

Original language description

Object pose estimation is a prominent task in computer vision. The object pose gives the orientation and translation of the object in real-world space, which allows various applications such as manipulation, augmented reality, etc. Various objects exhibit different properties with light, such as reflections, absorption, etc. This makes it challenging to understand the object’s structure in RGB and depth channels. Recent research has been moving toward learning-based methods, which provide a more flexible and generalizable approach to object pose estimation utilizing deep learning. One such approach is the render-and-compare method, which renders the object from multiple views and compares it against the given 2D image, which often requires an object representation in the form of a CAD model. We reason that the synthetic texture of the CAD model may not be ideal for rendering and comparing operations. We showed that if the object is represented as an implicit (neural) representation in the form of Neural Radiance Field (NeRF), it exhibits a more realistic rendering of the actual scene and retains the crucial spatial features, which makes the comparison more versatile. We evaluated our NeRF implementation of the render-and-compare method on transparent datasets and found that it surpassed the current state-of-the-art results.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20204 - Robotics and automatic control

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Computer Vision – ECCV 2024, Part LXXVIII

ISBN

978-3-031-91568-0

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

22

Pages from-to

226-247

Publisher name

Springer Nature

Place of publication

—

Event location

Milano

Event date

Sep 29, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001544976800015