[HTML][HTML] Generative adversarial network: An overview of theory and applications
In recent times, image segmentation has been involving everywhere including disease
diagnosis to autonomous vehicle driving. In computer vision, this image segmentation is one …
diagnosis to autonomous vehicle driving. In computer vision, this image segmentation is one …
Linking points with labels in 3D: A review of point cloud semantic segmentation
Ripe with possibilities offered by deep-learning techniques and useful in applications
related to remote sensing, computer vision, and robotics, 3D point cloud semantic …
related to remote sensing, computer vision, and robotics, 3D point cloud semantic …
Sceneverse: Scaling 3d vision-language learning for grounded scene understanding
Abstract 3D vision-language (3D-VL) grounding, which aims to align language with 3D
physical environments, stands as a cornerstone in developing embodied agents. In …
physical environments, stands as a cornerstone in developing embodied agents. In …
A survey on deep learning techniques for image and video semantic segmentation
Image semantic segmentation is more and more being of interest for computer vision and
machine learning researchers. Many applications on the rise need accurate and efficient …
machine learning researchers. Many applications on the rise need accurate and efficient …
Deep learning on 3D point clouds
A point cloud is a set of points defined in a 3D metric space. Point clouds have become one
of the most significant data formats for 3D representation and are gaining increased …
of the most significant data formats for 3D representation and are gaining increased …
[HTML][HTML] DILF: Differentiable rendering-based multi-view Image–Language Fusion for zero-shot 3D shape understanding
Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not
present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has …
present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has …
Deep learning on point clouds and its application: A survey
W Liu, J Sun, W Li, T Hu, P Wang - Sensors, 2019 - mdpi.com
Point cloud is a widely used 3D data form, which can be produced by depth sensors, such
as Light Detection and Ranging (LIDAR) and RGB-D cameras. Being unordered and …
as Light Detection and Ranging (LIDAR) and RGB-D cameras. Being unordered and …
A review on deep learning techniques for 3D sensed data classification
D Griffiths, J Boehm - Remote Sensing, 2019 - mdpi.com
Over the past decade deep learning has driven progress in 2D image understanding.
Despite these advancements, techniques for automatic 3D sensed data understanding, such …
Despite these advancements, techniques for automatic 3D sensed data understanding, such …
Recent advancements in learning algorithms for point clouds: An updated overview
Recent advancements in self-driving cars, robotics, and remote sensing have widened the
range of applications for 3D Point Cloud (PC) data. This data format poses several new …
range of applications for 3D Point Cloud (PC) data. This data format poses several new …
Gal: Geometric adversarial loss for single-view 3d-object reconstruction
In this paper, we present a framework for reconstructing a point-based 3D model of an object
from a single view image. Distance metrics, like Chamfer distance, were used in previous …
from a single view image. Distance metrics, like Chamfer distance, were used in previous …