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 …
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 …
Pcn: Point completion network
Shape completion, the problem of estimating the complete geometry of objects from partial
observations, lies at the core of many vision and robotics applications. In this work, we …
observations, lies at the core of many vision and robotics applications. In this work, we …
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 …
Pmp-net: Point cloud completion by learning multi-step point moving paths
The task of point cloud completion aims to predict the missing part for an incomplete 3D
shape. A widely used strategy is to generate a complete point cloud from the incomplete …
shape. A widely used strategy is to generate a complete point cloud from the incomplete …
Pmp-net++: Point cloud completion by transformer-enhanced multi-step point moving paths
Point cloud completion concerns to predict missing part for incomplete 3D shapes. A
common strategy is to generate complete shape according to incomplete input. However …
common strategy is to generate complete shape according to incomplete input. However …
Point cloud completion by skip-attention network with hierarchical folding
Point cloud completion aims to infer the complete geometries for missing regions of 3D
objects from incomplete ones. Previous methods usually predict the complete point cloud …
objects from incomplete ones. Previous methods usually predict the complete point cloud …
Semantic scene completion from a single depth image
This paper focuses on semantic scene completion, a task for producing a complete 3D voxel
representation of volumetric occupancy and semantic labels for a scene from a single-view …
representation of volumetric occupancy and semantic labels for a scene from a single-view …
Shape completion using 3d-encoder-predictor cnns and shape synthesis
We introduce a data-driven approach to complete partial 3D shapes through a combination
of volumetric deep neural networks and 3D shape synthesis. From a partially-scanned input …
of volumetric deep neural networks and 3D shape synthesis. From a partially-scanned input …
Sketchygan: Towards diverse and realistic sketch to image synthesis
Synthesizing realistic images from human drawn sketches is a challenging problem in
computer graphics and vision. Existing approaches either need exact edge maps, or rely on …
computer graphics and vision. Existing approaches either need exact edge maps, or rely on …