2 research outputs found
ViewSynth: Learning Local Features from Depth using View Synthesis
The rapid development of inexpensive commodity depth sensors has made
keypoint detection and matching in the depth image modality an important
problem in computer vision. Despite great improvements in recent RGB local
feature learning methods, adapting them directly in the depth modality leads to
unsatisfactory performance. Most of these methods do not explicitly reason
beyond the visible pixels in the images. To address the limitations of these
methods, we propose a framework ViewSynth, to jointly learn: (1) viewpoint
invariant keypoint-descriptor from depth images using a proposed Contrastive
Matching Loss, and (2) view synthesis of depth images from different viewpoints
using the proposed View Synthesis Module and View Synthesis Loss. By learning
view synthesis, we explicitly encourage the feature extractor to encode
information about not only the visible, but also the occluded parts of the
scene. We demonstrate that in the depth modality, ViewSynth outperforms the
state-of-the-art depth and RGB local feature extraction techniques in the 3D
keypoint matching and camera localization tasks on the RGB-D datasets 7-Scenes,
TUM RGBD and CoRBS in most scenarios. We also show the generalizability of
ViewSynth in 3D keypoint matching across different datasets.Comment: Accepted to BMVC 202
A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image
For 3D hand and body pose estimation task in depth image, a novel
anchor-based approach termed Anchor-to-Joint regression network (A2J) with the
end-to-end learning ability is proposed. Within A2J, anchor points able to
capture global-local spatial context information are densely set on depth image
as local regressors for the joints. They contribute to predict the positions of
the joints in ensemble way to enhance generalization ability. The proposed 3D
articulated pose estimation paradigm is different from the state-of-the-art
encoder-decoder based FCN, 3D CNN and point-set based manners. To discover
informative anchor points towards certain joint, anchor proposal procedure is
also proposed for A2J. Meanwhile 2D CNN (i.e., ResNet-50) is used as backbone
network to drive A2J, without using time-consuming 3D convolutional or
deconvolutional layers. The experiments on 3 hand datasets and 2 body datasets
verify A2J's superiority. Meanwhile, A2J is of high running speed around 100
FPS on single NVIDIA 1080Ti GPU.Comment: Accepted by ICCV201