title: Active Neural Mapping
summary: We address the problem of active mapping with a continually-learned neural
  scene representation, namely Active Neural Mapping. The key lies in actively
  finding the target space to be explored with efficient agent movement, thus
  minimizing the map uncertainty on-the-fly within a previously unseen
  environment. In this paper, we examine the weight space of the
  continually-learned neural field, and show empirically that the neural
  variability, the prediction robustness against random weight perturbation, can
  be directly utilized to measure the instant uncertainty of the neural map.
  Together with the continuous geometric information inherited in the neural map,
  the agent can be guided to find a traversable path to gradually gain knowledge
  of the environment. We present for the first time an active mapping system with
  a coordinate-based implicit neural representation for online scene
  reconstruction. Experiments in the visually-realistic Gibson and Matterport3D
  environment demonstrate the efficacy of the proposed method.
paper_authors._id: 64f1a2da79dc71a66af7a439
paper_authors.name: Zike Yan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-10-04T07:37:58.923Z
paper_authors._id: 650ed24a2a45730c3fde9407
paper_authors.avatarUrl: /avatars/3f575f58b9546180a95aa3dafa028590.svg
paper_authors.fullname: Zike Yan
paper_authors.type: user
paper_authors.user: ZikeYan
paper_authors._id: 64f1a2da79dc71a66af7a43a
paper_authors.name: Haoxiang Yang
paper_authors._id: 64f1a2da79dc71a66af7a43b
paper_authors.name: Hongbin Zha
paper_title: Active Neural Mapping
paper_summary: We address the problem of active mapping with a continually-learned neural
  scene representation, namely Active Neural Mapping. The key lies in actively
  finding the target space to be explored with efficient agent movement, thus
  minimizing the map uncertainty on-the-fly within a previously unseen
  environment. In this paper, we examine the weight space of the
  continually-learned neural field, and show empirically that the neural
  variability, the prediction robustness against random weight perturbation, can
  be directly utilized to measure the instant uncertainty of the neural map.
  Together with the continuous geometric information inherited in the neural map,
  the agent can be guided to find a traversable path to gradually gain knowledge
  of the environment. We present for the first time an active mapping system with
  a coordinate-based implicit neural representation for online scene
  reconstruction. Experiments in the visually-realistic Gibson and Matterport3D
  environment demonstrate the efficacy of the proposed method.
paper_ai_keywords: neural scene representation Active Neural Mapping neural field neural variability weight space prediction robustness random Weight perturbation map uncertainty continuous geometric information traversable path coordinate-based implicit neural representation online scene reefficiency Gibson environment Matterport3D environment