Vojtěch Pánek presents DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization
On 2020-12-01 10:00
at https://meet.google.com/scp-cncb-txs
Online reading group on the work "DH3D: Deep Hierarchical 3D Descriptors for
Robust Large-Scale 6DoF Relocalization",
Juan Du, Rui Wang, Daniel Cremers, ECCV 2020
Video conference link: meet.google.com/scp-cncb-txs
Instructions: http://cmp.felk.cvut.cz/~toliageo/rg/instructions.html
Paper abstract: For relocalization in large-scale point clouds, we propose the
first approach that unifies global place recognition and local 6DoF pose
refinement. To this end, we design a Siamese network that jointly learns 3D
local feature detection and description directly from raw 3D points. It
integrates FlexConv and Squeeze-and-Excitation (SE) to assure that the learned
local descriptor captures multi-level geometric information and channel-wise
relations. For detecting 3D keypoints we predict the discriminativeness of the
local descriptors in an unsupervised manner. We generate the global descriptor
by directly aggregating the learned local descriptors with an effective
attention mechanism. In this way, local and global 3D descriptors are inferred
in one single forward pass. Experiments on various benchmarks demonstrate that
our method achieves competitive results for both global point cloud retrieval
and local point cloud registration in comparison to state-of-the-art
approaches.
To validate the generalizability and robustness of our 3D keypoints, we
demonstrate that our method also performs favorably without fine-tuning on the
registration of point clouds that were generated by a visual SLAM system. Code
and related materials are available at this https URL.
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.
See the page of reading groups
http://cmp.felk.cvut.cz/~toliageo/rg/index.html
Robust Large-Scale 6DoF Relocalization",
Juan Du, Rui Wang, Daniel Cremers, ECCV 2020
Video conference link: meet.google.com/scp-cncb-txs
Instructions: http://cmp.felk.cvut.cz/~toliageo/rg/instructions.html
Paper abstract: For relocalization in large-scale point clouds, we propose the
first approach that unifies global place recognition and local 6DoF pose
refinement. To this end, we design a Siamese network that jointly learns 3D
local feature detection and description directly from raw 3D points. It
integrates FlexConv and Squeeze-and-Excitation (SE) to assure that the learned
local descriptor captures multi-level geometric information and channel-wise
relations. For detecting 3D keypoints we predict the discriminativeness of the
local descriptors in an unsupervised manner. We generate the global descriptor
by directly aggregating the learned local descriptors with an effective
attention mechanism. In this way, local and global 3D descriptors are inferred
in one single forward pass. Experiments on various benchmarks demonstrate that
our method achieves competitive results for both global point cloud retrieval
and local point cloud registration in comparison to state-of-the-art
approaches.
To validate the generalizability and robustness of our 3D keypoints, we
demonstrate that our method also performs favorably without fine-tuning on the
registration of point clouds that were generated by a visual SLAM system. Code
and related materials are available at this https URL.
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.
See the page of reading groups
http://cmp.felk.cvut.cz/~toliageo/rg/index.html