Search Results for author:
Found 7 papers, 6 papers with code
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions.
Ranked #1 on
Semantic Segmentation
on S3DIS
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture.
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning
We then introduce a new Anisotropic Reduction function into our Separable SA module and propose an Anisotropic Separable SA (ASSA) module that substantially increases the network's accuracy.
Ranked #10 on
Semantic Segmentation
on S3DIS Area5
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN.
SGAS: Sequential Greedy Architecture Search
Architecture design has become a crucial component of successful deep learning.
Ranked #3 on
Node Classification
on PPI
DeepGCNs: Making GCNs Go as Deep as CNNs
This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.
Ranked #4 on
3D Semantic Segmentation
on PartNet
Rethinking the Pipeline of Demosaicing, Denoising and Super-Resolution
Such a mixture problem is usually solved by a sequential solution (applying each method independently in a fixed order: DM $\to$ DN $\to$ SR), or is simply tackled by an end-to-end network without enough analysis into interactions among tasks, resulting in an undesired performance drop in the final image quality.
