Recent years have produced a variety of learning based methods in the context of computer
vision and robotics. Most of the recently proposed methods are based on deep learning …

Robots operating in the real world are expected to detect, classify, segment, and estimate
the pose of objects to accomplish their task. Modern approaches using deep learning not …

Supervised learning relies heavily on labeled datasets, but data annotation is time-
consuming and tedious work. To speed up annotation, in this paper, we propose a fast data …

Manually annotating 3D point clouds is laborious and costly, limiting the training data
preparation for deep learning in real-world object detection. While a few previous studies …

Recent works on open-vocabulary 3D instance segmentation show strong promise, but at
the cost of slow inference speed and high computation requirements. This high computation …

Availability of a few, large-size, annotated datasets, like ImageNet, Pascal VOC and COCO,
has lead deep learning to revolutionize computer vision research by achieving astonishing …

Abstract 3D automatic annotation has received increased attention since manually
annotating 3D point clouds is laborious. However, existing methods are usually complicated …

Accurately annotating multiple 3D objects in LiDAR scenes is laborious and challenging.
While a few previous studies have attempted to leverage semi-automatic methods for cost …

Recent works on open-vocabulary 3D instance segmentation show strong promise, but at
the cost of slow inference speed and high computation requirements. This high computation …

Large-scale training data with high-quality annotations is critical for training semantic and
instance segmentation models. Unfortunately, pixel-wise annotation is labor-intensive and …