Active View Selection

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Overview

Active view selection studies how an agent can move a camera or sensor to gather useful evidence instead of observing from fixed viewpoints or scanning exhaustively. Methods estimate information value using uncertainty, Fisher information, predictive entropy, visibility and coverage, then choose a next-best view or plan a longer trajectory. The approach is used in 3D reconstruction with radiance fields and Gaussian splatting, as well as robot navigation, manipulation, medical imaging, and multi-view reasoning. A central challenge is balancing information gain with task relevance, view feasibility, computation, and the reliability of uncertain or generated observations.

Latest papers

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35 papers7 papers in the last four weeks, up 17% on the four weeks before. 0.0% of all new papers.

  1. OA-NBV: Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots

    Mar 10, 2026Boxun Hu, Chang Chang, Jiawei Ge +4Active PerceptionNext-Best-View Planning

    Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA · Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA · Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA +1

  2. Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction

    Mar 6, 2026Yulun Wu, Ruyi Zha, Wei Cao +33D Gaussian SplattingActive Perception

    University of Illinois Urbana-Champaign · Australian National University · Johns Hopkins University

  3. I-Perceive: A Foundation Model for Vision-Language Active Perception

    Feb 28, 2026Yongxi Huang, Zhuohang Wang, Wenjing Tang +33D Scene UnderstandingRobot Foundation Models

    Shanghai Jiao Tong University · Shanghai Innovation Institute · Beihang University

  4. B3^3-Seg: Camera-Free, Training-Free 3DGS Segmentation via Analytic EIG and Beta-Bernoulli Bayesian Updates

    Feb 19, 2026Hiromichi Kamata, Samuel Arthur Munro, Fuminori Homma3DGS SegmentationOpen-Vocabulary 3D Segmentation

    Sony Group Corporation, Tokyo, Japan · Pixomondo, London, United Kingdom

  5. DAV-GSWT: Diffusion-Active-View Sampling for Data-Efficient Gaussian Splatting Wang Tiles

    Feb 17, 2026Rong Fu, Jiekai Wu, Yang Li +33D Scene Generation3D Gaussian Splatting

    Independent Researcher