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Found 5 papers, 1 papers with code
CWF: Consolidating Weak Features in High-quality Mesh Simplification
In mesh simplification, common requirements like accuracy, triangle quality, and feature alignment are often considered as a trade-off.
VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data
A multi-stream convolutional neural network is proposed to learn the 3D volume and 2D MIP features respectively and then explore their inter-dependencies in a joint volume-composition embedding space by unprojecting the MIP features into 3D volume embedding space.
Attention-based Deep Reinforcement Learning for Multi-view Environments
In reinforcement learning algorithms, it is a common practice to account for only a single view of the environment to make the desired decisions; however, utilizing multiple views of the environment can help to promote the learning of complicated policies.
A-CNN: Annularly Convolutional Neural Networks on Point Clouds
Analyzing the geometric and semantic properties of 3D point clouds through the deep networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures.
Ranked #35 on
Semantic Segmentation
on S3DIS
Directionally Convolutional Networks for 3D Shape Segmentation
Previous approaches on 3D shape segmentation mostly rely on heuristic processing and hand-tuned geometric descriptors.
