講演動画はこちら:
https://youtu.be/BoUNuMJGHuc
講演者:
斎藤 修(Epic Games Japan)
https://twitter.com/shiba_zushi
本スライドは2021年7月25日に行われたオンライン勉強会「UE4 Character Art Dive Online」の講演資料となります。
イベントについてはこちら:
https://www.unrealengine.com/ja/blog/epicgamesjapan-onlinelearning-13
講演動画はこちら:
https://youtu.be/BoUNuMJGHuc
講演者:
斎藤 修(Epic Games Japan)
https://twitter.com/shiba_zushi
本スライドは2021年7月25日に行われたオンライン勉強会「UE4 Character Art Dive Online」の講演資料となります。
イベントについてはこちら:
https://www.unrealengine.com/ja/blog/epicgamesjapan-onlinelearning-13
Recipes for Optical Effect System Design - Real-time Rendering of Physically...Silicon Studio Corporation
These slides are a portion of the lecture on "Real-Time Rendering of Physically Based Optical Effects in Theory and Practice" at Siggraph 2015. The whole course is available on the tri-Ace web site. All of the Silicon Studio slides are available from our web site.
Silicon Studio: http://www.siliconstudio.co.jp/rd/presentations/
tri-Ace: http://research.tri-ace.com/s2015.html
このスライドはSIGGRAPH2015のCourse「Real-Time Rendering of Physically Based Optical Effects in Theory and Practice」の講演資料の一部です。Course全体のスライドはトライエースのWebサイトに掲載されています。元のスライドデータは、シリコンスタジオまたはトライエースのWebサイトからダウンロードできます。
シリコンスタジオ:http://www.siliconstudio.co.jp/rd/presentations/
トライエース:http://research.tri-ace.com/s2015.html
Lenses - Real-time Rendering of Physically Based Optical Effect in Theory an...Silicon Studio Corporation
These slides are a portion of the lecture on "Real-Time Rendering of Physically Based Optical Effects in Theory and Practice" at Siggraph 2015. The whole course is available on the tri-Ace web site. All of the Silicon Studio slides are available from our web site.
Silicon Studio: http://www.siliconstudio.co.jp/rd/presentations/
tri-Ace: http://research.tri-ace.com/s2015.html
このスライドはSIGGRAPH2015のCourse「Real-Time Rendering of Physically Based Optical Effects in Theory and Practice」の講演資料の一部です。Course全体のスライドはトライエースのWebサイトに掲載されています。元のスライドデータは、シリコンスタジオまたはトライエースのWebサイトからダウンロードできます。
シリコンスタジオ:http://www.siliconstudio.co.jp/rd/presentations/
トライエース:http://research.tri-ace.com/s2015.html
Subtle Anamorphic Lens Effects - Real-time Rendering of Physically Based Opt...Silicon Studio Corporation
These slides are a portion of the lecture on "Real-Time Rendering of Physically Based Optical Effects in Theory and Practice" at Siggraph 2015. The whole course is available on the tri-Ace web site. All of the Silicon Studio slides are available from our web site.
Silicon Studio: http://www.siliconstudio.co.jp/rd/presentations/
tri-Ace: http://research.tri-ace.com/s2015.html
このスライドはSIGGRAPH2015のCourse「Real-Time Rendering of Physically Based Optical Effects in Theory and Practice」の講演資料の一部です。Course全体のスライドはトライエースのWebサイトに掲載されています。元のスライドデータは、シリコンスタジオまたはトライエースのWebサイトからダウンロードできます。
シリコンスタジオ:http://www.siliconstudio.co.jp/rd/presentations/
トライエース:http://research.tri-ace.com/s2015.html
Making Your Bokeh Fascinating - Real-time Rendering of Physically Based Opti...Silicon Studio Corporation
These slides are a portion of the lecture on "Real-Time Rendering of Physically Based Optical Effects in Theory and Practice" at Siggraph 2015. The whole course is available on the tri-Ace web site. All of the Silicon Studio slides are available from our web site.
Silicon Studio: http://www.siliconstudio.co.jp/rd/presentations/
tri-Ace: http://research.tri-ace.com/s2015.html
このスライドはSIGGRAPH2015のCourse「Real-Time Rendering of Physically Based Optical Effects in Theory and Practice」の講演資料の一部です。Course全体のスライドはトライエースのWebサイトに掲載されています。元のスライドデータは、シリコンスタジオまたはトライエースのWebサイトからダウンロードできます。
シリコンスタジオ:http://www.siliconstudio.co.jp/rd/presentations/
トライエース:http://research.tri-ace.com/s2015.html
[2010]
Large-scale Image Classification: Fast Feature Extraction and SVM Training
[2011]
High-dimensional signature compression for large-scale image classification
文献紹介:TinyVIRAT: Low-resolution Video Action RecognitionToru Tamaki
Ugur Demir, Yogesh S Rawat, Mubarak Shah, TinyVIRAT: Low-resolution Video Action Recognition, ICPR2021, pp. 7387-7394
doi: 10.1109/ICPR48806.2021.9412541
https://www.computer.org/csdl/proceedings-article/icpr/2021/09412541/1tmi1dyHSEg
https://arxiv.org/abs/2007.07355
In mobile social games, reducing user attrition, i.e. churn, is decisive to increase player retention and rise revenues. Survival analysis focuses on predicting the time of occurrence of a certain event and efficiently deals with the censored data problem, which is in the nature of churn. However, due to the inflexibility of the traditional survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results.
In this talk, we present a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn for each player, as a function of time and game level. Firstly, we describe that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses. Secondly, we discuss these results in the framework of Game Data Science as a Service. The goal of Silicon Studio is to Democratize Game Data Science. Hence, the proposed method is able to make predictions in an operational business environment and easily adapts to different kinds of games, players, and therefore distributions of the data. We focus on a flexible technique that does not need a previous manipulation of the data and that is able to deal efficiently with the temporal dimension of the churn prediction problem.
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using ...Silicon Studio Corporation
Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results. In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression.
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
145. Shading/Lighting 参考文献
[McAuley12] Stephen McAuley, “Calibrating Lighting and Materials in Far Cry 3”, SIGGRAPH 2012 Course: Practical Physically Based
Shading in Film and Game Production
[Meyer10] Quirin Meyer1, Jochen Süßmuth1, Gerd Sußner2, Marc Stamminger1 and Günther Greiner1 “On Floating-Point Normal Vectors”
Eurographics Symposium on Rendering 2010
[Karis13] Brian Karis, “Real Shading in Unreal Engine 4”, SIGGRAPH 2013 Course: Physically Based Shading in Theory and Practice
[Neubelt13] David Neubelt, Matt Pettineo, “Crafting a Next-Gen Material Pipeline for The Order: 1886”, SIGGRAPH 2013 Course: Physically
Based Shading in Theory and Practice
[Valient14] Michal Valient “Taking Killzone Shadow Fall Image Quality into the Next Generation”, GDC 2014
[Robison09] Austin Robison, Peter Shirly , “Image Space Gathering”, HPG 2009
[Gotanda2013] Yoshiharu Gotanda, “Physically Based Reflectance Model Design for Next Generation”, CEDEC 2013
[Gotanda2011] Yoshiharu Gotanda, “Real-time Physically Based Rendering - Implementation”, CEDEC 2011
[Uludag14] Yasin Uludag, “Hi-Z Screen-Space Cone-Traced Reflections” GPU Pro 5
[Sousa13] Tiago Sousa, “Graphics Gems from CryENGINE 3”, Siggraph 2013 Advances in Real-Time Rendering in Games Course
[Lagarde12] Sébastien Lagarde “Lighting approaches and parallax-corrected cubemap”
http://seblagarde.wordpress.com/2012/09/29/image-based-lighting-approaches-and-parallax-corrected-cubemap/
[ Toksvig04 ] Michael Toksvig, "Mipmapping Normal Maps", nVIDIA Technical Brief
146. ポストエフェクト 参考文献
[Karis14] Brian Karis, “High Quality Temporal Supersampling”, SIGGRAPH 2014 Course: Advances in Real-time Rendering in
Games: Part I
[Wroński14] Bartłomiej Wroński, “Temporal supersampling and antialiasing”, http://bartwronski.com/2014/03/15/temporal-
supersampling-and-antialiasing/
[Kawase08] Masaki Kawase, “レンダリスト養成講座 2.0 ~光学に基づいたボケ味の表現~”, CEDEC 2008
[Kawase09] Masaki Kawase, “続・レンダリスト養成講座 ~アンチ縮小バッファアーティファクト~”, CEDEC 2009
[Kawase12] Masaki Kawase, “実践!シネマティックレンズエフェクト ー光学に基づいたボケ味の表現ー”, CEDEC 2012