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Welcome


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Welcome to the Computer Vision Group at RWTH Aachen University!

The Computer Vision group has been established at RWTH Aachen University in context with the Cluster of Excellence "UMIC - Ultra High-Speed Mobile Information and Communication" and is associated with the Chair Computer Sciences 8 - Computer Graphics, Computer Vision, and Multimedia. The group focuses on computer vision applications for mobile devices and robotic or automotive platforms. Our main research areas are visual object recognition, tracking, self-localization, 3D reconstruction, and in particular combinations between those topics.

We offer lectures and seminars about computer vision and machine learning.

You can browse through all our publications and the projects we are working on.

News

We won the ECCV2020 "3D Poses in the Wild" Challenge!

See the MeTRAbs paper on our approach and check out the code on GitHub.

Aug. 23, 2020

BMVC'20

We have one paper accepted at the British Machine Vision Conference (BMVC) 2020:

July 29, 2020

ECCV'20

We have two papers accepted at the European Conference on Computer Vision (ECCV) 2020:

June 24, 2020

We have one paper accepted at the Workshop on Towards Human-Centric Image/Video Synthesis, IEEE Conference on Computer Vision and Pattern Recognition (CVPRW'20)

April 21, 2020

CVPR'20

We have three papers accepted at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020:

Feb. 24, 2020

ICRA'20

We have three accepted papers at the International Conference on Robotics and Automation (ICRA) 2020:

Jan. 31, 2020

Recent Publications

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STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos

ECCV '20

Existing methods for instance segmentation in videos typically involve multi-stage pipelines that follow the tracking-by-detection paradigm and model a video clip as a sequence of images. Multiple networks are used to detect objects in individual frames, and then associate these detections over time. Hence, these methods are often non-end-to-end trainable and highly tailored to specific tasks. In this paper, we propose a different approach that is well-suited to a variety of tasks involving instance segmentation in videos. In particular, we model a video clip as a single 3D spatio-temporal volume, and propose a novel approach that segments and tracks instances across space and time in a single stage. Our problem formulation is centered around the idea of spatio-temporal embeddings which are trained to cluster pixels belonging to a specific object instance over an entire video clip. To this end, we introduce (i) novel mixing functions that enhance the feature representation of spatio-temporal embeddings, and (ii) a single-stage, proposal-free network that can reason about temporal context. Our network is trained end-to-end to learn spatio-temporal embeddings as well as parameters required to cluster these embeddings, thus simplifying inference. Our method achieves state-of-the-art results across multiple datasets and tasks.

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3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020

We present 3D-MPA, a method for instance segmentation on 3D point clouds. Given an input point cloud, we propose an object-centric approach where each point votes for its object center. We sample object proposals from the predicted object centers. Then we learn proposal features from grouped point features that voted for the same object center. A graph convolutional network introduces inter-proposal relations, providing higher-level feature learning in addition to the lower-level point features. Each proposal comprises a semantic label, a set of associated points over which we define a foreground-background mask, an objectness score and aggregation features. Previous works usually perform non-maximum-suppression (NMS) over proposals to obtain the final object detections or semantic instances. However, NMS can discard potentially correct predictions. Instead, our approach keeps all proposals and groups them together based on the learned aggregation features. We show that grouping proposals improves over NMS and outperforms previous state-of-the-art methods on the tasks of 3D object detection and semantic instance segmentation on the ScanNetV2 benchmark and the S3DIS dataset.

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DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020 (Oral)

We propose DualConvMesh-Nets (DCM-Net) a family of deep hierarchical convolutional networks over 3D geometric data that *combines two types* of convolutions. The first type, *geodesic convolutions*, defines the kernel weights over mesh surfaces or graphs. That is, the convolutional kernel weights are mapped to the local surface of a given mesh. The second type, *Euclidean convolutions*, is independent of any underlying mesh structure. The convolutional kernel is applied on a neighborhood obtained from a local affinity representation based on the Euclidean distance between 3D points. Intuitively, geodesic convolutions can easily separate objects that are spatially close but have disconnected surfaces, while Euclidean convolutions can represent interactions between nearby objects better, as they are oblivious to object surfaces. To realize a multi-resolution architecture, we borrow well-established mesh simplification methods from the geometry processing domain and adapt them to define mesh-preserving pooling and unpooling operations. We experimentally show that combining both types of convolutions in our architecture leads to significant performance gains for 3D semantic segmentation, and we report competitive results on three scene segmentation benchmarks.

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