RESEARCH

COMPUTER VISION

Occupancy Anticipation for Efficient Exploration and Navigation

August 21, 2020

Abstract

State-of-the-art navigation methods leverage a spatial memory to generalize to new environments, but their occupancy maps are limited to capturing the geometric structures directly observed by the agent. We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions. In doing so, the agent builds its spatial awareness more rapidly, which facilitates efficient exploration and navigation in 3D environments. By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment, with performance significantly better than strong baselines. Furthermore, when deployed for the sequential decision-making tasks of exploration and navigation, our model outperforms state-of-the-art methods on the Gibson and Matterport3D datasets. Our approach is the winning entry in the 2020 Habitat PointNav Challenge. Project page: http://vision.cs.utexas.edu/projects/occupancy_anticipation/

Download the Paper

AUTHORS

Written by

Kristen Grauman

Santhosh Kumar Ramakrishnan

Santhosh Ramakrishnan

Ziad Al-Halah

Publisher

ECCV

Research Topics

Computer Vision

Related Publications

December 11, 2024

COMPUTER VISION

Measuring Deja Vu Memorization Efficiently

Narine Kokhlikyan, Bargav Jayaraman, Florian Bordes, Chuan Guo, Kamalika Chaudhuri

December 11, 2024

November 20, 2024

CONVERSATIONAL AI

COMPUTER VISION

Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations

Jianfeng Chi, Ujjwal Karn, Hongyuan Zhan, Eric Smith, Javier Rando, Yiming Zhang, Kate Plawiak, Zacharie Delpierre Coudert, Kartikeya Upasani, Mahesh Pasupuleti

November 20, 2024

November 11, 2024

COMPUTER VISION

HOI-Swap: Swapping Objects in Videos with Hand-Object Interaction Awareness

Sherry Xue, Romy Luo, Changan Chen, Kristen Grauman

November 11, 2024

October 31, 2024

HUMAN & MACHINE INTELLIGENCE

ROBOTICS

Digitizing Touch with an Artificial Multimodal Fingertip

Mike Lambeta, Tingfan Wu, Ali Sengül, Victoria Rose Most, Nolan Black, Kevin Sawyer, Romeo Mercado, Haozhi Qi, Alexander Sohn, Byron Taylor, Norb Tydingco, Gregg Kammerer, Dave Stroud, Jake Khatha, Kurt Jenkins, Kyle Most, Neal Stein, Ricardo Chavira, Thomas Craven-Bartle, Eric Sanchez, Yitian Ding, Jitendra Malik, Roberto Calandra

October 31, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.