Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Continual Neural Mapping: Learning An Implicit Scene Representation from
  Sequential Observations   Recent advances have enabled a single neural network to serve as an implicit
scene representation, establishing the mapping function between spatial
coordinates and scene properties. In this paper, we make a further step towards
continual learning of the implicit scene representation directly from
sequential observations, namely Continual Neural Mapping. The proposed problem
setting bridges the gap between batch-trained implicit neural representations
and commonly used streaming data in robotics and vision communities. We
introduce an experience replay approach to tackle an exemplary task of
continual neural mapping: approximating a continuous signed distance function
(SDF) from sequential depth images as a scene geometry representation. We show
for the first time that a single network can represent scene geometry over time
continually without catastrophic forgetting, while achieving promising
trade-offs between accuracy and efficiency.

2
The categories of this paper are: math
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph