The measurement rate of the event-based camera is on the order of a microsecond, its independent pixel architecture provides very high dynamic range, and the bandwidth of an event stream is much lower than a standard video stream. These superior properties of event-based cameras over the potential to overcome some limitation of conventional cameras Facts : 1673 registered persons 415 accepted papers (26,6%) 342 posters (21,9%) 45 spotlights (2,9%) 28 orals (1,8%) “Top-down Neural Attention by Excitation Backprop”, by Jianming Zhang, Zhe Lin, Jonathan Brandt Xiaohui Shen, and Stan Sclaroff A new backpropagation scheme, Excitation Backprop, based on a probabilistic Winner- Take-All formulation is proposed to model the top-down neural attention for CNN classifiers. Authors also presents a contrastive top-down attention, which captures the differential effect between a pair of contrastive top-down signals. This contrastive top- down attention can significantly improve the discriminativeness of the generated attention maps. European Conference On Computer Vision ECCV ’16 Trends 3DVTech Trends Report Amsterdam Octobre 2016 Short version Best papers CNN reduction Human pose SFM-MVS Other “Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera” by Hanme Kim, Stefan Leutenegger and Andrew Davison” from Imperial College London; This paper presents a method which can perform real-time 3D reconstruction from a single hand-held event camera with no additional sensing, and works in unstructured scenes of which it has no prior knowledge. It is based on three decoupled probabilistic filters, each estimating 6-DoF camera motion, scene logarithmic (log) intensity gradient and scene inverse depth relative to a keyframe. They build a real-time graph of these to track and model over an extended local workspace. Downloadable Codes & links • ECCV ’16 conference: http://eccv2016.org/ • SSD: Single Shot MultiBox Detector: SSD is a unified framework for object detection with a single network. You can use the code to train/evaluate a network for object detection task. https://github.com/weiliu89/caffe/tree/ssd Download this report at http://www.3dvtech.com/ Longer version or specific review of the conference can be asked, please contact 3DVTech. Best Student Award Emma Alexander for Focal Flow: Measuring Distance and Velocity with Defocus and Differential Motion She presents a new system for perceiving depth: the focal Flow sensor. It is an unactuated, monocular camera that simultaneously exploits defocus and differential motion to measure a depth map and a 3D scene velocity field. It does so using an optical- flow-like, per-pixel linear constraint that relates image derivatives to depth and velocity. Best paper awards. 3DVTech trends report – ECCV ’16 – Short version 3DVTech - CréACannes 11 avenue Maurice Chevalier 06150 Cannes La Bocca Téléphone : 06 21 13 81 28 Email : [email protected]www.3DVTech.com See: https://youtu.be/yHLyhdMSw7w • Pixelwise View Selection for Unstructured Multi-View Stereo: COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline for robust and dense modeling from unstructured images. https://colmap.github.io
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The measurement rate of the event-based camera is on the order of a microsecond, its independent pixel architecture provides very high dynamic range, and the bandwidth of an event stream is much lower than a standard video stream. These superior properties of event-based cameras over the potential to overcome some limitation of conventional cameras
by Jianming Zhang, Zhe Lin, Jonathan Brandt Xiaohui Shen, and Stan Sclaroff
A new backpropagation scheme, Excitation Backprop, based on a probabilistic Winner-Take-All formulation is proposed to model the top-down neural attention for CNN classifiers. Authors also presents a contrastive top-down attention, which captures the differential effect between a pair of contrastive top-down signals. This contrastive top-down attention can significantly improve the discriminativeness of the generated attention maps.
European Conference
On Computer Vision
ECCV ’16 Trends 3DV T e c h
Tr e n d s Re p or t
Am s t er d am Oc t o br e
2 01 6
Short version
Best papers
CNN reduction
Human pose
SFM-MVS
Other
“Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera” by Hanme Kim, Stefan Leutenegger and Andrew Davison” from Imperial College London; This paper presents a method which can perform real-time 3D reconstruction from a single hand-held event camera with no additional sensing, and works in unstructured scenes of which it has no prior knowledge. It is based on three decoupled probabilistic filters, each estimating 6-DoF camera motion, scene logarithmic (log) intensity gradient and scene inverse depth relative to a keyframe. They build a real-time graph of these to track and model over an extended local workspace.
Downloadable Codes & links
• ECCV ’16 conference: http://eccv2016.org/
• SSD: Single Shot MultiBox Detector: SSD is a unified framework for object detection with a single
network. You can use the code to train/evaluate a network for object
detection task.
https://github.com/weiliu89/caffe/tree/ssd
Download this report at http://www.3dvtech.com/
Longer version or specific review of the conference can be asked, please contact 3DVTech.