Virtual KITTI 2
원문 초록
This paper introduces an updated version of the well-known Virtual KITTI dataset which consists of 5 sequence clones from the KITTI tracking benchmark. In addition, the dataset provides different variants of these sequences such as modified weather conditions (e.g. fog, rain) or modified camera configurations (e.g. rotated by 15 degrees). For each sequence, we provide multiple sets of images containing RGB, depth, class segmentation, instance segmentation, flow, and scene flow data. Camera parameters and poses as well as vehicle locations are available as well. In order to showcase some of the dataset's capabilities, we ran multiple relevant experiments using state-of-the-art algorithms from the field of autonomous driving. The dataset is available for download at https://europe.naverlabs.com/Research/Computer-Vision/Proxy-Virtual-Worlds.
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A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color
No time to train! Training-Free Reference-Based Instance Segmentation
NOCTIS: Novel Object Cyclic Threshold based Instance Segmentation
SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation
Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation
NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models
토론
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