Computer vision technology is moving more and more towards a three-dimensional
approach, and plant phenoty** is following this trend. However, despite its potential, the …

As a bridge between physical objects and as-built models, image-based 3D reconstruction
performs a vital role by generating point cloud models, mesh models, textured models, and …

Raw point clouds data inevitably contains outliers or noise through acquisition from 3D
sensors or reconstruction algorithms. In this paper, we present a novel end-to-end network …

Point clouds acquired from scanning devices are often perturbed by noise, which affects
downstream tasks such as surface reconstruction and analysis. The distribution of a noisy …

Three-dimensional point cloud registration is an important field in computer vision. Recently,
due to the increasingly complex scenes and incomplete observations, many partial-overlap …

Vehicle detection with visual sensors like lidar and camera is one of the critical functions
enabling autonomous driving. While they generate fine-grained point clouds or high …

In this paper, we introduce Point2Mesh, a technique for reconstructing a surface mesh from
an input point cloud. Instead of explicitly specifying a prior that encodes the expected shape …

Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent
upsampling approaches aim to generate a dense point set, while achieving both distribution …

We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for
point clouds. Gradients for point locations and normals are carefully designed to handle …

A key step in any scanning-based asset creation workflow is to convert unordered point
clouds to a surface. Classical methods (eg, Poisson reconstruction) start to degrade in the …