Autowareにおける三次元物体認識アルゴリズム「PointPillars」の紹介 - TIER IV Tech Blog
こんにちは。 ティアフォーで自動運転ソフトウェア開発を行っている村上です。 今回はDeep Learningを使った三次元物体認識の手法を紹介していきます。 TL;DR: 12msで動作する三次元物体認識アルゴリズムの開発 自動運転におけるDeep Learning 点群を処理するためのDeep Learning ざっと従来手法 従来手法での問題 形状推定の必要性 Deep Learningで可能なこと 三次元物体認識アルゴリズム「PointPillars」の紹介 ざっと類似手法 なぜ「PointPillars」 CUDAとTensorRTによる高速化 最後に 自動運転におけるDeep Learning 自動運転では主に周りの環境を認識する際にDeep Learningを用いることが多いです。画像認識アルゴリズムであるSSD*1やYOLO*2が有名なものになります。 Deep Learni
AI Habitat
📢 Habitat 3.0 is out. Find more information on the project page 📢 Habitat Navigation Challenge 2023 is live now! You can find it here What is Embodied AI? “AI is … the science and engineering of making intelligent machines.” - John McCarthy Embodied AI is the science and engineering of intelligent machines with a physical or virtual embodiment (typically, robots and egocentric personal assistant
MIRU 2018 - 各チームの発表資料
A: Data Mining データマイニング研究の最新動向: 時系列、テキストマイニング、異常検知 B: HCI ユーザ評価から見るHCI -良いシステムの実現のためにCV研究者が学ぶこと- C: Robotics 実ロボットの知識獲得のためのシミュレーションを用いた転移学習における環境差異の解決 D: Bio CVにおける「認識」タスクに向けた生物の視覚情報の獲得とその処理についての調査 E: ASP 音声処理の最先端 F: NLP CV2NLP：コンピュータビジョンと自然言語処理の共存マップ G: Psychology A survey on Psychology - Connecting Perception, Language, and Memory studies with Computer Vision
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution Uber uses convolutional neural networks in many domains that could potentially involve coordinate transforms, from designing self-driving vehicles to automating street sign detection to build maps and maximizing the efficiency of spatial movements in the Uber Marketplace. In deep learning, few ideas have experienced
Neural scene representation and rendering
Research Neural scene representation and rendering Published 14 June 2018 Authors Ali Eslami, Danilo Jimenez Rezende There is more than meets the eye when it comes to how we understand a visual scene: our brains draw on prior knowledge to reason and to make inferences that go far beyond the patterns of light that hit our retinas. For example, when entering a room for the first time, you instantly
Learning to navigate in cities without a map
Research Learning to navigate in cities without a map Published 29 March 2018 Authors Piotr Mirowski, Raia Hadsell, Andrew Zisserman How did you learn to navigate the neighborhood of your childhood, to go to a friend’s house, to your school or to the grocery store? Probably without a map and simply by remembering the visual appearance of streets and turns along the way. As you gradually explored y
jingwan-lu
Jingwan joined Adobe Research in 2014. She is currently leading a team of research scientists and engineers with the vision to disrupt visual content creation with big data and generative AI. Her research interests include generative image modeling (GANs, etc.), computational photography, digital human and data-driven artistic content creation. She has over 30 issued patents and over 40 publicatio
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[DL輪読会]Learning agile and dynamic motor skills for legged robots
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping · Issue #425 · arXivTimes/arXivTimes
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