Search Results for author:
Found 10 papers, 6 papers with code
Deep Reinforcement Learning Based Toolpath Generation for Thermal Uniformity in Laser Powder Bed Fusion Process
Although various scan patterns have been studied to reduce possible accumulated stress, such as zigzag scanning vectors with changing directions or a chessboard-based scan pattern with divided small islands, most conventional scan patterns cannot significantly reduce residual stress.
HRBF-Fusion: Accurate 3D reconstruction from RGB-D data using on-the-fly implicits
However, due to the discrete nature and limited resolution of their surface representations (e. g., point- or voxel-based), existing approaches suffer from the accumulation of errors in camera tracking and distortion in the reconstruction, which leads to an unsatisfactory 3D reconstruction.
Memory-Efficient Modeling and Slicing of Large-Scale Adaptive Lattice Structures
The presented methods have been validated by a series of case studies with large number (up to 100M) of struts to demonstrate its applicability to large-scale lattice structures.
Computational Geometry
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution
Next, we propose a step-wise fusion strategy to restore the HR depth map.
A framework for adaptive width control of dense contour-parallel toolpaths in fused deposition modeling
In this paper we present a framework which supports multiple schemes to generate toolpaths with adaptive width, by employing a function to decide the number of beads and their widths.
Graphics Robotics Systems and Control Systems and Control J.6
Learning to Accelerate Decomposition for Multi-Directional 3D Printing
Different printing directions are employed in different regions to fabricate a model with tremendously less support (or even no support in many cases). To obtain optimized decomposition, a large beam width needs to be used in the search algorithm, leading to a very time-consuming computation.
Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive Convolution
To process the input images in low resolution with missing regions, new operators for adaptive convolution are introduced in our deep-learning network that consists of three cascaded modules -- the completion module, the refinement module and the super-resolution module.
Concise and Effective Network for 3D Human Modeling from Orthogonal Silhouettes
In this paper, we revisit the problem of 3D human modeling from two orthogonal silhouettes of individuals (i. e., front and side views).
Photometric Stereo With Near Point Lighting: A Solution by Mesh Deformation
We tackle the problem of photometric stereo under near point lighting in this paper.
Surface-from-Gradients: An Approach Based on Discrete Geometry Processing
In this paper, we propose an efficient method to reconstruct surface-from-gradients (SfG).
