Active Neural Mapping
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.
64f1a2da79dc71a66af7a439
Zike Yan
claimed_verified
2023-10-04T07:37:58.923Z
650ed24a2a45730c3fde9407
/avatars/3f575f58b9546180a95aa3dafa028590.svg
Zike Yan
user
ZikeYan
64f1a2da79dc71a66af7a43a
Haoxiang Yang
64f1a2da79dc71a66af7a43b
Hongbin Zha
Active Neural Mapping
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.
The paper introduces Active Neural Mapping, a method using a coordinate-based implicit neural representation to guide an agent for active exploration and mapping in unseen environments, leveraging the uncertainty in the neural field weights and geometric information.
active mapping neural scene representation active neural mapping neural field neural variability prediction robustness weight perturbation instant uncertainty neural map coordinate-based implicit neural representation online scene reconstruction