Search Results for author: Arnie Sen

Found 5 papers, 1 papers with code

LOC-ZSON: Language-driven Object-Centric Zero-Shot Object Retrieval and Navigation

no code implementations8 May 2024 Tianrui Guan, Yurou Yang, Harry Cheng, Muyuan Lin, Richard Kim, Rajasimman Madhivanan, Arnie Sen, Dinesh Manocha

In this paper, we present LOC-ZSON, a novel Language-driven Object-Centric image representation for object navigation task within complex scenes.

Language Modelling Object +1

PoCo: Point Context Cluster for RGBD Indoor Place Recognition

no code implementations3 Apr 2024 Jing Liang, Zhuo Deng, Zheming Zhou, Omid Ghasemalizadeh, Dinesh Manocha, Min Sun, Cheng-Hao Kuo, Arnie Sen

We present a novel end-to-end algorithm (PoCo) for the indoor RGB-D place recognition task, aimed at identifying the most likely match for a given query frame within a reference database.

Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior

no code implementations15 Oct 2023 Xiaotong Chen, Zheming Zhou, Zhuo Deng, Omid Ghasemalizadeh, Min Sun, Cheng-Hao Kuo, Arnie Sen

Reconstructing transparent objects using affordable RGB-D cameras is a persistent challenge in robotic perception due to inconsistent appearances across views in the RGB domain and inaccurate depth readings in each single-view.

3D Reconstruction Depth Completion +3

Learning to View: Decision Transformers for Active Object Detection

no code implementations23 Jan 2023 Wenhao Ding, Nathalie Majcherczyk, Mohit Deshpande, Xuewei Qi, Ding Zhao, Rajasimman Madhivanan, Arnie Sen

Active perception describes a broad class of techniques that couple planning and perception systems to move the robot in a way to give the robot more information about the environment.

Active Object Detection Motion Planning +5

SupeRGB-D: Zero-shot Instance Segmentation in Cluttered Indoor Environments

1 code implementation22 Dec 2022 Evin Pınar Örnek, Aravindhan K Krishnan, Shreekant Gayaka, Cheng-Hao Kuo, Arnie Sen, Nassir Navab, Federico Tombari

We introduce a zero-shot split for Tabletop Objects Dataset (TOD-Z) to enable this study and present a method that uses annotated objects to learn the ``objectness'' of pixels and generalize to unseen object categories in cluttered indoor environments.

Instance Segmentation Object +2

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