Jan Kúdelka presents Convolutional Occupancy Networks

On 2021-12-07 11:00:00 at https://feectu.zoom.us/j/98555944426
Online reading group on the work "Convolutional Occupancy Networks", ECCV 2020

Paper abstract: Recently, implicit neural representations have gained
for learning-based 3D reconstruction. While demonstrating promising results,
most implicit approaches are limited to comparably simple geometry of single
objects and do not scale to more complicated or large-scale scenes. The key
limiting factor of implicit methods is their simple fully-connected network
architecture which does not allow for integrating local information in the
observations or incorporating inductive biases such as translational
equivariance. In this paper, we propose Convolutional Occupancy Networks, a
flexible implicit representation for detailed reconstruction of objects and 3D
scenes. By combining convolutional encoders with implicit occupancy decoders,
our model incorporates inductive biases, enabling structured reasoning in 3D
space. We investigate the effectiveness of the proposed representation by
reconstructing complex geometry from noisy point clouds and low-resolution
representations. We empirically find that our method enables the fine-grained
implicit 3D reconstruction of single objects, scales to large indoor scenes,
generalizes well from synthetic to real data.

Paper url: https://arxiv.org/abs/2003.04618

Instructions for participants: The reading group studies the literature in the
field of pattern recognition and computer vision. At each meeting one or more
papers are prepared for presentation by a single person, the presenter. The
meetings are open to anyone, disregarding their background. It is assumed that
everyone attending the reading group has, at least briefly, read the paper –
not necessarily understanding everything. Attendants should preferably send
questions about the unclear parts to the speaker at least one day in advance.
During the presentation we aim to have a fruitful discussion, a critical
analysis of the paper, as well as brainstorming for creative extensions.

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Responsible person: Petr Pošík