전체 글150 [백준] 21608 - 상어 초등학교 (Python) 문제 출처 : www.acmicpc.net/problem/21608 21608번: 상어 초등학교 상어 초등학교에는 교실이 하나 있고, 교실은 N×N 크기의 격자로 나타낼 수 있다. 학교에 다니는 학생의 수는 N2명이다. 오늘은 모든 학생의 자리를 정하는 날이다. 학생은 1번부터 N2번까지 번호 www.acmicpc.net 문제 풀이 : 삼성전자 2021년도 상반기 오전 문제중 첫번째 문제이다. 삼성전자의 기출문제를 보았다면 대부분 BFS,DFS + 구현 문제로 이루어져 있는 것을 알 수 있다. 때문에 보통 필자는 삼성전자의 문제를 풀땐 어떤 부분들을 구현해야하는지 나누는걸 첫번째로 한다. 요즘 추세로는 구현해야하는 것들을 문제에서 친절하게 주어주는 경우도 많다. 때문에 문제를 잘 읽어보는 것이 굉장히 중.. 2021. 5. 10. [Paper Review] Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving 논문 : Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving URL : arxiv.org/abs/1812.07179 Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving 3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input da.. 2021. 5. 1. [Paper Review] MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization 논문 : MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization URL : arxiv.org/abs/1811.10247 MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a single RGB image due to the geometric information loss duri.. 2021. 5. 1. [Paper Review] GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving 논문 : GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving URL : arxiv.org/abs/1903.10955 GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving. Our efforts are put on extracting the underlying 3D information in a 2D image and determining the.. 2021. 5. 1. [Paper Review] Disentangling Monocular 3D Object Detection (MonoDis) 논문 : Disentangling Monocular 3D Object Detection (MonoDis) URL : arxiv.org/abs/1905.12365 Disentangling Monocular 3D Object Detection In this paper we propose an approach for monocular 3D object detection from a single RGB image, which leverages a novel disentangling transformation for 2D and 3D detection losses and a novel, self-supervised confidence score for 3D bounding boxes. Our pro arxiv.o.. 2021. 5. 1. [Paper Review] Pyramid Stereo Matching Network(PSMNet) 논문 : Pyramid Stereo Matching Network (PSMNet) URL : arxiv.org/abs/1803.08669 Pyramid Stereo Matching Network Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs). However, current architectures rely on patch-based Siamese networks, lacking arxiv.org 저자 : Jia-Ren Chang, Yon.. 2021. 5. 1. 이전 1 2 3 4 5 6 7 8 ··· 25 다음 반응형
