We propose 3DSmoothNet, a full workflow to match 3D point clouds with a siamese deep
learning architecture and fully convolutional layers using a voxelized smoothed density value (…

We introduce PREDATOR, a model for pairwise pointcloud registration with deep attention
to the overlap region. Different from previous work, our model is specifically designed to …

We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm.
Registration of multiple scans typically follows a two-stage pipeline: the initial pairwise …

Areal deformation monitoring based on point clouds can be a very valuable alternative to
the established point-based monitoring techniques, especially for deformation monitoring of …

We propose a novel fully automated deformation analysis pipeline capable of estimating real
3D displacement vectors from point cloud data. Different from the traditional methods that …

As several industries are moving towards modeling massive 3D virtual worlds, the need for
content creation tools that can scale in terms of the quantity, quality, and diversity of 3D …

Denoising diffusion models (DDMs) have shown promising results in 3D point cloud
synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high …

Reconstruction and intrinsic decomposition of scenes from captured imagery would enable
many applications such as relighting and virtual object insertion. Recent NeRF based …

We present a novel method for reconstructing a 3D implicit surface from a large-scale, sparse,
and noisy point cloud. Our approach builds upon the recently introduced Neural Kernel …

We present Neural Fields for LiDAR (NFL), a method to optimise a neural field scene
representation from LiDAR measurements, with the goal of synthesizing realistic LiDAR scans from …