LiDARism

[ECCV 2024] Rethinking LiDAR Domain Generalization: Single Source as Multiple Density Domains paper link: https://arxiv.org/abs/2312.12098 Rethinking LiDAR Domain Generalization: Single Source as Multiple Density DomainsIn the realm of LiDAR-based perception, significant strides have been made, yet domain generalization remains a substantial challenge. The performance often deteriorates when mod..

Best Time to Buy and Sell Stock - LeetCodeYou are given an array prices where prices[i] is the price of a given stock on the ith day.You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.이번에는 최대 이득을 볼 수 있는 주..

문제 링크: https://leetcode.com/problems/two-sum/description/ 문제는 다음과 같다.Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target. You may assume that each input would have exactly one solution, and you may not use the same element twice. You can return the answer in any order.complement 만드는 것 제외하면 별 방법이 떠오르지 않아서 그냥 brute force처럼 했는데, e..

이번 ECCV 2024에 accept된 저의 "Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather"가 oral paper로 선정되었습니다!!!너무 기분 좋아용

[2407.02286] Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather (arxiv.org) Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse WeatherExisting LiDAR semantic segmentation methods often struggle with performance declines in adverse weather conditions. Previous work has addressed this issue by simulating adverse weather or employing u..

[2008.05711] Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D (arxiv.org) Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3DThe goal of perception for autonomous vehicles is to extract semantic representations from multiple sensors and fuse these representations into a single "bird's-eye-view" coordinate ..

IntroductionNeRF는 outlier에 취약하다는 문제가 있었다. 이를 해결하기 위해 depth 정보를 주고 동시에 depth estimator의 uncertainty 측정 기법을 활용하면 NeRF의 문제를 해결하면서 동시에 SLAM을 수행할 수 있다.최종적으로 NeRF와 uncertainty information을 동시에 활용하는 SLAM method를 제안한다.MethodologyTracking: Dense SLAM with Covariances이 부분은 설명이 좀 어려운 것 같다...큰 틀은 DROID-SLAM을 따른다. 이미지 두개를 받아서 optical flow를 만들고, 그 optical flow를 기반으로 depth를 추정한다. 그리고 이 과정에서 local BA (Bundle Ad..

Introduction기존 방법은 EKF나 optimization based method로 나뉜다. 여기서 optimization의 핵심은 full Bundle Adjustment (BA)이다. 이는 camera pose와 3D map을 동시에 최적화한다.이런 optimization 방법은 여러 종류의 센서를 활용하기에 적합하다는 것이다.For example, ORB-SLAM3 [5] supports monocular, stereo, RGB-D, and IMU sensors, and modern systems can support a variety of camera models [5, 27, 43, 6].하지만 여전히 feature tracking을 실패하거나, drift error를 완전히 해결하지 못..

Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather Introduction & Related Works The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs. While existing methods exploit redundant information in good environm..

CycleGAN: Unpaired Image-to-Image Translationusing Cycle-Consistent Adversarial Networks Introduction & Related Works Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. Image-to-image translation은 말 그대로 pair image 간의 transformation function을 만드는 task이다..