OPEN SOURCE blenderpy Mesh/Voxel Visualization Figure 1 (click to enlarge): Visualization examples of an occupancy grid (left) and a mesh (right) of a chair. The right visualization also shows a point cloud observation (in red). Blender is an open-source “3D creation suite” — a tool for creating and manipulating 3D shapes and scenes. While I […]
Obtaining high-quality visualizations of 3D data such as triangular meshes or occupancy grids, as needed for publications in computer graphics and computer vision, is difficult. In this article, I want to present a GitHub repository containing some utility scripts for paper-ready visualizations of meshes and occupancy grids using Blender and Python.
Our CVPR’18 follow-up paper has been accepted at IJCV. In this longer paper we extend our weakly-supervised 3D shape completion approach to obtain high-quality shape predictions, and also present updated, synthetic benchmarks on ShapeNet and ModelNet. The paper is available through Springer Link and ArXiv.
In 3D vision, a common problem involves the comparison of meshes. In 3D reconstruction or surface reconstruction, triangular meshes are usually compared considering accuracy and completeness — the distance from the reconstruction to the reference and vice-versa. In this article, I want to present an efficient C++ tool for computing accuracy and completeness considering both references meshes as well as reference point clouds.
Triangular meshes are commonly used to represent various shapes in computer graphics and computer vision. However, for various deep learning techniques, triangular meshes are not well suited. Therefore, meshes are commonly voxelized into occupancy grids or signed distance functions. This article presents a C++ tool allowing efficient voxelization of (watertight) meshes.
Automatically obtaining high-quality watertight meshes in order to derive well-defined occupancy grids or signed distance functions is a common problem in 3D vision. In this article, I present a mesh fusion approach for obtaining watertight meshes. In combination with a standard mesh simplification algorithm, this approach produces high-quality, but lightweight, watertight meshes.
In this follow-up on our CVPR’18 work, we extend our weakly-supervised 3D shape completion approach to obtain high-quality shape predictions, and also present updated, synthetic benchmarks on ShapeNet and ModelNet. The paper is now available as pre-print on ArXiv. Abstract, some experimental results and a comparison to our CVPR’18 work can be found in this article.
In this CVPR’18 paper, based on my master thesis, we propose a weakly-supervised and learning-based approach to 3D shape completion of sparse and noisy point clouds. We show that, using a learned shape prior, shape completion can be learned without access to ground truth shapes — only by knowing the object category at hand. This article provides the paper and its supplementary material.
My master thesis, written at the Autonomous Vision Group of Max Planck Institute for Intelligent Systems under the supervision of Prof. Andreas Geiger, addresses the problem of 3D shape completion of sparse point clouds under weak supervision. Specifically, based on a learned shape prior it is possible to learn 3D shape completion without access to ground truth shapes, as shown on KITTI. This article briefly introduces the problem and the main contributions and offers the thesis as download.
In computer graphics, watertight meshes usually describe meshes consisting of one closed surface. In this sense, watertight meshes do not contain holes and have a clearly defined inside. Therefore, they are commonly required by many applications in computer graphics as well as in computer vision — for example, when voxelizing meshes into occupancy grids or signed distance functions. However, I found it very difficult to find a proper formal definition of watertightness. In this article, I want to discuss the definition I used for my master thesis.