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Found 5 papers, 4 papers with code
Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance
To this end, we propose a search scheme, {\em Loss Distillation via Gradient Matching}, to find good candidate loss functions by mimicking the learning behavior in backpropagation between HyperCD and weighted CD.
Understanding Hyperbolic Metric Learning through Hard Negative Sampling
In recent years, there has been a growing trend of incorporating hyperbolic geometry methods into computer vision.
Hyperbolic Contrastive Learning
Learning good image representations that are beneficial to downstream tasks is a challenging task in computer vision.
Hyperbolic Chamfer Distance for Point Cloud Completion
Chamfer distance (CD) is a standard metric to measure the shape dissimilarity between point clouds in point cloud completion, as well as a loss function for (deep) learning.
FPCC: Fast Point Cloud Clustering based Instance Segmentation for Industrial Bin-picking
Based on this task requirement, we propose a Fast Point Cloud Clustering (FPCC) for instance segmentation of bin-picking scene.
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