Search Results for author:
Found 9 papers, 4 papers with code
PermutoSDF: Fast Multi-View Reconstruction with Implicit Surfaces using Permutohedral Lattices
We propose improvements to the two areas by replacing the voxel hash encoding with a permutohedral lattice which optimizes faster, especially for higher dimensions.
Neural Strands: Learning Hair Geometry and Appearance from Multi-View Images
Furthermore, we introduce a novel neural rendering framework based on rasterization of the learned hair strands.
Abstract Flow for Temporal Semantic Segmentation on the Permutohedral Lattice
Semantic segmentation is a core ability required by autonomous agents, as being able to distinguish which parts of the scene belong to which object class is crucial for navigation and interaction with the environment.
LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices
Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images.
NeuralMVS: Bridging Multi-View Stereo and Novel View Synthesis
Our method uses only a sparse set of images as input and can generalize well to novel scenes.
Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching
We integrate both into stereo estimation as well as visual odometry systems and show clear benefits for typical disparity and direct image registration tasks when using our proposed metric.
Bonn Activity Maps: Dataset Description
The key prerequisite for accessing the huge potential of current machine learning techniques is the availability of large databases that capture the complex relations of interest.
LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices
Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images.
Ranked #30 on
3D Semantic Segmentation
on SemanticKITTI
Semi-Supervised Semantic Mapping through Label Propagation with Semantic Texture Meshes
We propose to represent the semantic map as a geometrical mesh and a semantic texture coupled at independent resolution.
