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Found 6 papers, 2 papers with code
Learning Reconstructability for Drone Aerial Path Planning
To reconstruct a new urban scene, we first build the 3D scene proxy, then rely on the predicted reconstruction quality and uncertainty measures by our network, based off of the proxy geometry, to guide the drone path planning.
ShapeFormer: Transformer-based Shape Completion via Sparse Representation
We present ShapeFormer, a transformer-based network that produces a distribution of object completions, conditioned on incomplete, and possibly noisy, point clouds.
Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset
We present UrbanScene3D, a large-scale data platform for research of urban scene perception and reconstruction.
Hausdorff Point Convolution with Geometric Priors
We show that HPC constitutes a powerful point feature learning with a rather compact set of only four types of geometric priors as kernels.
On Learning the Right Attention Point for Feature Enhancement
We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e. g., classification and segmentation.
Transferable Multi-level Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multi-task Learning
The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science.
Ranked #7 on
Formation Energy
on QM9
