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Chapter and Conference Paper
Language-Grounded Indoor 3D Semantic Segmentation in the Wild
Recent advances in 3D semantic segmentation with deep neural networks have shown remarkable success, with rapid performance increase on available datasets. However, current 3D semantic segmentation benchmarks ...
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Chapter and Conference Paper
MvDeCor: Multi-view Dense Correspondence Learning for Fine-Grained 3D Segmentation
We propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks. This is inspired by the observation that view-based surface representations are more effective at...
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Chapter and Conference Paper
Towards Precise Completion of Deformable Shapes
According to Aristotle, “the whole is greater than the sum of its parts”. This statement was adopted to explain human perception by the Gestalt psychology school of thought in the twentieth century. Here, we clai...
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Chapter and Conference Paper
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
Arguably one of the top success stories of deep learning is transfer learning. The finding that pre-training a network on a rich source set (e.g., ImageNet) can help boost performance once fine-tuned on a usually...
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Chapter and Conference Paper
White Matter Fiber Representation Using Continuous Dictionary Learning
With increasingly sophisticated Diffusion Weighted MRI acquisition methods and modeling techniques, very large sets of streamlines (fibers) are presently generated per imaged brain. These reconstructions of wh...
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Chapter and Conference Paper
Putting the Pieces Together: Regularized Multi-part Shape Matching
Multi-part shape matching is an important class of problems, arising in many fields such as computational archaeology, biology, geometry processing, computer graphics and vision. In this paper, we address the ...
