11,357 research outputs found
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles
Autonomous driving (AD) perception today relies heavily on deep learning
based architectures requiring large scale annotated datasets with their
associated costs for curation and annotation. The 3D semantic data are useful
for core perception tasks such as obstacle detection and ego-vehicle
localization. We propose a new dataset, Navya 3D Segmentation (Navya3DSeg),
with a diverse label space corresponding to a large scale production grade
operational domain, including rural, urban, industrial sites and universities
from 13 countries. It contains 23 labeled sequences and 25 supplementary
sequences without labels, designed to explore self-supervised and
semi-supervised semantic segmentation benchmarks on point clouds. We also
propose a novel method for sequential dataset split generation based on
iterative multi-label stratification, and demonstrated to achieve a +1.2% mIoU
improvement over the original split proposed by SemanticKITTI dataset. A
complete benchmark for semantic segmentation task was performed, with state of
the art methods. Finally, we demonstrate an active learning (AL) based dataset
distillation framework. We introduce a novel heuristic-free sampling method
called distance sampling in the context of AL. A detailed presentation on the
dataset is available at https://www.youtube.com/watch?v=5m6ALIs-s20 .Comment: Submitted to RA-L. Version with supplementary material
R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild
Semantic understanding of roadways is a key enabling factor for safe
autonomous driving. However, existing autonomous driving datasets provide
well-structured urban roads while ignoring unstructured roadways containing
distress, potholes, water puddles, and various kinds of road patches i.e.,
earthen, gravel etc. To this end, we introduce Road Region Segmentation dataset
(R2S100K) -- a large-scale dataset and benchmark for training and evaluation of
road segmentation in aforementioned challenging unstructured roadways. R2S100K
comprises 100K images extracted from a large and diverse set of video sequences
covering more than 1000 KM of roadways. Out of these 100K privacy respecting
images, 14,000 images have fine pixel-labeling of road regions, with 86,000
unlabeled images that can be leveraged through semi-supervised learning
methods. Alongside, we present an Efficient Data Sampling (EDS) based
self-training framework to improve learning by leveraging unlabeled data. Our
experimental results demonstrate that the proposed method significantly
improves learning methods in generalizability and reduces the labeling cost for
semantic segmentation tasks. Our benchmark will be publicly available to
facilitate future research at https://r2s100k.github.io/
Matterport3D: Learning from RGB-D Data in Indoor Environments
Access to large, diverse RGB-D datasets is critical for training RGB-D scene
understanding algorithms. However, existing datasets still cover only a limited
number of views or a restricted scale of spaces. In this paper, we introduce
Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views
from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided
with surface reconstructions, camera poses, and 2D and 3D semantic
segmentations. The precise global alignment and comprehensive, diverse
panoramic set of views over entire buildings enable a variety of supervised and
self-supervised computer vision tasks, including keypoint matching, view
overlap prediction, normal prediction from color, semantic segmentation, and
region classification
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