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 …
Single image 3D object reconstruction based on deep learning: A review
K Fu, J Peng, Q He, H Zhang - Multimedia Tools and Applications, 2021 - Springer
The reconstruction of 3D object from a single image is an important task in the field of
computer vision. In recent years, 3D reconstruction of single image using deep learning …
computer vision. In recent years, 3D reconstruction of single image using deep learning …
Pointr: Diverse point cloud completion with geometry-aware transformers
Point clouds captured in real-world applications are often incomplete due to the limited
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …
Autosdf: Shape priors for 3d completion, reconstruction and generation
Powerful priors allow us to perform inference with insufficient information. In this paper, we
propose an autoregressive prior for 3D shapes to solve multimodal 3D tasks such as shape …
propose an autoregressive prior for 3D shapes to solve multimodal 3D tasks such as shape …
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 …
Shapeformer: Transformer-based shape completion via sparse representation
We present ShapeFormer, a transformer-based network that produces a distribution of
object completions, conditioned on incomplete, and possibly noisy, point clouds. The …
object completions, conditioned on incomplete, and possibly noisy, point clouds. The …
Unsupervised point cloud pre-training via occlusion completion
We describe a simple pre-training approach for point clouds. It works in three steps: 1. Mask
all points occluded in a camera view; 2. Learn an encoder-decoder model to reconstruct the …
all points occluded in a camera view; 2. Learn an encoder-decoder model to reconstruct the …
Variational relational point completion network
Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise.
Existing point cloud completion methods tend to generate global shape skeletons and …
Existing point cloud completion methods tend to generate global shape skeletons and …
Seedformer: Patch seeds based point cloud completion with upsample transformer
Point cloud completion has become increasingly popular among generation tasks of 3D
point clouds, as it is a challenging yet indispensable problem to recover the complete shape …
point clouds, as it is a challenging yet indispensable problem to recover the complete shape …
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 …