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Found 7 papers, 5 papers with code
Learning Deformable Hypothesis Sampling for Accurate PatchMatch Multi-View Stereo
This paper introduces a learnable Deformable Hypothesis Sampler (DeformSampler) to address the challenging issue of noisy depth estimation for accurate PatchMatch Multi-View Stereo (MVS).
Shape Anchor Guided Holistic Indoor Scene Understanding
This paper proposes a shape anchor guided learning strategy (AncLearn) for robust holistic indoor scene understanding.
Holistic Geometric Feature Learning for Structured Reconstruction
The inference of topological principles is a key problem in structured reconstruction.
HoW-3D: Holistic 3D Wireframe Perception from a Single Image
This paper studies the problem of holistic 3D wireframe perception (HoW-3D), a new task of perceiving both the visible 3D wireframes and the invisible ones from single-view 2D images.
A multi-domain splitting framework for time-varying graph structure
To address the anomaly detection problem for datasets with a spatial-temporal structure, in this work, we propose a novel graph multi-domain splitting framework, called GMDS, by integrating the time, vertex, and frequency features to locate the anomalies.
Unmixing Convolutional Features for Crisp Edge Detection
This paper presents a context-aware tracing strategy (CATS) for crisp edge detection with deep edge detectors, based on an observation that the localization ambiguity of deep edge detectors is mainly caused by the mixing phenomenon of convolutional neural networks: feature mixing in edge classification and side mixing during fusing side predictions.
Ranked #2 on
Edge Detection
on MDBD
ELKPPNet: An Edge-aware Neural Network with Large Kernel Pyramid Pooling for Learning Discriminative Features in Semantic Segmentation
In this paper, a practical and efficient edge-aware neural network is presented for semantic segmentation.
