Comprehensive review of deep learning-based 3d point cloud completion processing and analysis
Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …
Unsupervised point cloud representation learning with deep neural networks: A survey
Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …
Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders
Pre-training by numerous image data has become de-facto for robust 2D representations. In
contrast, due to the expensive data processing, a paucity of 3D datasets severely hinders …
contrast, due to the expensive data processing, a paucity of 3D datasets severely hinders …
Multimodal virtual point 3d detection
Lidar-based sensing drives current autonomous vehicles. Despite rapid progress, current
Lidar sensors still lag two decades behind traditional color cameras in terms of resolution …
Lidar sensors still lag two decades behind traditional color cameras in terms of resolution …
Snowflakenet: Point cloud completion by snowflake point deconvolution with skip-transformer
Point cloud completion aims to predict a complete shape in high accuracy from its partial
observation. However, previous methods usually suffered from discrete nature of point cloud …
observation. However, previous methods usually suffered from discrete nature of point cloud …
Semantic segmentation for real point cloud scenes via bilateral augmentation and adaptive fusion
Given the prominence of current 3D sensors, a fine-grained analysis on the basic point
cloud data is worthy of further investigation. Particularly, real point cloud scenes can …
cloud data is worthy of further investigation. Particularly, real point cloud scenes can …
Spg: Unsupervised domain adaptation for 3d object detection via semantic point generation
In autonomous driving, a LiDAR-based object detector should perform reliably at different
geographic locations and under various weather conditions. While recent 3D detection …
geographic locations and under various weather conditions. While recent 3D detection …
Score-based point cloud denoising
Point clouds acquired from scanning devices are often perturbed by noise, which affects
downstream tasks such as surface reconstruction and analysis. The distribution of a noisy …
downstream tasks such as surface reconstruction and analysis. The distribution of a noisy …
Ag3d: Learning to generate 3d avatars from 2d image collections
While progress in 2D generative models of human appearance has been rapid, many
applications require 3D avatars that can be animated and rendered. Unfortunately, most …
applications require 3D avatars that can be animated and rendered. Unfortunately, most …
Grnet: Gridding residual network for dense point cloud completion
Estimating the complete 3D point cloud from an incomplete one is a key problem in many
vision and robotics applications. Mainstream methods (eg, PCN and TopNet) use Multi-layer …
vision and robotics applications. Mainstream methods (eg, PCN and TopNet) use Multi-layer …