IAM

Check out our latest research on adversarial robustness and generalization of deep networks.

TAG»COMPUTER GRAPHICS«

ARTICLE

IJCV Paper “Learning 3D Shape Completion under Weak Supervision”

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.

More ...

ARTICLE

A C++ Implementation of Mesh-to-Mesh Distance

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.

More ...

ARTICLE

Mesh Voxelization into Occupancy Grids and Signed Distance Functions

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.

More ...

ARTICLE

Watertight Meshes by Mesh Fusion

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.

More ...

ARTICLE

ArXiv Pre-Print “Learning 3D Shape Completion under Weak Supervision”

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.

More ...

ARTICLE

CVPR’18 Paper “Learning 3D Shape Completion from Laser Scan Data with Weak Supervision”

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.

More ...

ARTICLE

Master Thesis “Learning Shape Completion from Bounding Boxes using CAD Shape Priors”

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.

More ...

ARTICLE

A Formal Definition of Watertight Meshes

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.

More ...

ARTICLE

Visualizing Triangular Meshes from .off Files using Python occmodel

This article discusses how to visualize triangular meshes available in Object File Format (.off) in Python using occmodel. Installation instructions for installing occmodel on Ubuntu are included.

More ...