no code implementations • 24 Jul 2024 • Simone Müller, Daniel Kolb, Matthias Müller, Dieter Kranzlmüller
With the help of this information, causally related actions can be adapted to different circumstances.
no code implementations • 13 May 2024 • Matthias Müller, Samarth Brahmbhatt, Ankur Deka, Quentin Leboutet, David Hafner, Vladlen Koltun
All materials can be found at https://www. openbot. org.
no code implementations • 28 Mar 2024 • Yujin Chen, Yinyu Nie, Benjamin Ummenhofer, Reiner Birkl, Michael Paulitsch, Matthias Müller, Matthias Nießner
In Mesh2NeRF, we propose an analytic solution to directly obtain ground-truth radiance fields from 3D meshes, characterizing the density field with an occupancy function featuring a defined surface thickness, and determining view-dependent color through a reflection function considering both the mesh and environment lighting.
1 code implementation • 16 Feb 2024 • Xuelun Shen, Zhipeng Cai, Wei Yin, Matthias Müller, Zijun Li, Kaixuan Wang, Xiaozhi Chen, Cheng Wang
Given an architecture, GIM first trains it on standard domain-specific datasets and then combines it with complementary matching methods to create dense labels on nearby frames of novel videos.
Ranked #1 on
Pose Estimation
on InLoc
1 code implementation • 13 Dec 2023 • Mykola Lavreniuk, Shariq Farooq Bhat, Matthias Müller, Peter Wonka
Second, we propose a novel image-text alignment module for improved feature extraction of the Stable Diffusion backbone.
Ranked #1 on
Depth Estimation
on NYU-Depth V2
no code implementations • 1 Dec 2023 • Botos Csaba, Wenxuan Zhang, Matthias Müller, Ser-Nam Lim, Mohamed Elhoseiny, Philip Torr, Adel Bibi
We introduce a new continual learning framework with explicit modeling of the label delay between data and label streams over time steps.
1 code implementation • NeurIPS 2023 • Xiuhong Lin, Changjie Qiu, Zhipeng Cai, Siqi Shen, Yu Zang, Weiquan Liu, Xuesheng Bian, Matthias Müller, Cheng Wang
While registration of 2D RGB images to 3D point clouds is a long-standing problem in computer vision, no prior work studies 2D-3D registration for event cameras.
2 code implementations • 26 Jul 2023 • Reiner Birkl, Diana Wofk, Matthias Müller
We release MiDaS v3. 1 for monocular depth estimation, offering a variety of new models based on different encoder backbones.
Ranked #5 on
Monocular Depth Estimation
on ETH3D
1 code implementation • 10 Apr 2023 • Motasem Alfarra, Hani Itani, Alejandro Pardo, Shyma Alhuwaider, Merey Ramazanova, Juan C. Pérez, Zhipeng Cai, Matthias Müller, Bernard Ghanem
To address this issue, we propose a more realistic evaluation protocol for TTA methods, where data is received in an online fashion from a constant-speed data stream, thereby accounting for the method's adaptation speed.
1 code implementation • 21 Mar 2023 • Diana Wofk, René Ranftl, Matthias Müller, Vladlen Koltun
We evaluate on the TartanAir and VOID datasets, observing up to 30% reduction in inverse RMSE with dense scale alignment relative to performing just global alignment alone.
6 code implementations • 23 Feb 2023 • Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, Matthias Müller
Finally, ZoeD-M12-NK is the first model that can jointly train on multiple datasets (NYU Depth v2 and KITTI) without a significant drop in performance and achieve unprecedented zero-shot generalization performance to eight unseen datasets from both indoor and outdoor domains.
Ranked #23 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
1 code implementation • 29 Jan 2023 • Samarth Brahmbhatt, Ankur Deka, Andrew Spielberg, Matthias Müller
In this paper we train a contact-exploiting manipulation policy in simulation for the contact-rich household task of loading plates into a slotted holder, which transfers without any fine-tuning to the real robot.
no code implementations • 29 Nov 2022 • Motasem Alfarra, Zhipeng Cai, Adel Bibi, Bernard Ghanem, Matthias Müller
This work explores the problem of Online Domain-Incremental Continual Segmentation (ODICS), where the model is continually trained over batches of densely labeled images from different domains, with limited computation and no information about the task boundaries.
1 code implementation • 26 Sep 2022 • Nina Wiedemann, Valentin Wüest, Antonio Loquercio, Matthias Müller, Dario Floreano, Davide Scaramuzza
Conversely, learning-based offline optimization approaches, such as Reinforcement Learning (RL), allow fast and efficient execution on the robot but hardly match the accuracy of MPC in trajectory tracking tasks.
1 code implementation • 11 Oct 2021 • Antonio Loquercio, Elia Kaufmann, René Ranftl, Matthias Müller, Vladlen Koltun, Davide Scaramuzza
Indeed, the subtasks are executed sequentially, leading to increased processing latency and a compounding of errors through the pipeline.
no code implementations • 29 Sep 2021 • Nina Wiedemann, Antonio Loquercio, Matthias Müller, Rene Ranftl, Davide Scaramuzza
We evaluate our approach on several complex systems and tasks, and experimentally analyze the advantages over model-free and model-based methods in terms of performance and sample efficiency.
4 code implementations • 14 Jun 2021 • Guohao Li, Matthias Müller, Bernard Ghanem, Vladlen Koltun
Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
1 code implementation • 24 Aug 2020 • Matthias Müller, Vladlen Koltun
We develop a software stack that allows smartphones to use this body for mobile operation and demonstrate that the system is sufficiently powerful to support advanced robotics workloads such as person following and real-time autonomous navigation in unstructured environments.
1 code implementation • 10 Jun 2020 • Elia Kaufmann, Antonio Loquercio, René Ranftl, Matthias Müller, Vladlen Koltun, Davide Scaramuzza
In this paper, we propose to learn a sensorimotor policy that enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation.
Robotics
1 code implementation • CVPR 2020 • Guohao Li, Guocheng Qian, Itzel C. Delgadillo, Matthias Müller, Ali Thabet, Bernard Ghanem
Architecture design has become a crucial component of successful deep learning.
Ranked #4 on
Node Classification
on PPI
4 code implementations • 15 Oct 2019 • Guohao Li, Matthias Müller, Guocheng Qian, Itzel C. Delgadillo, Abdulellah Abualshour, Ali Thabet, Bernard Ghanem
This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.
Ranked #5 on
3D Semantic Segmentation
on PartNet
no code implementations • 18 Apr 2019 • Matthias Müller, Guohao Li, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
A common approach is to learn an end-to-end policy that directly predicts controls from raw images by imitating an expert.
1 code implementation • ICCV 2019 • Guohao Li, Matthias Müller, Ali Thabet, Bernard Ghanem
Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3. 7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation.
1 code implementation • 5 Dec 2018 • Abdullah Hamdi, Matthias Müller, Bernard Ghanem
In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks.
no code implementations • 25 Apr 2018 • Matthias Müller, Alexey Dosovitskiy, Bernard Ghanem, Vladlen Koltun
Simulation can help end-to-end driving systems by providing a cheap, safe, and diverse training environment.
1 code implementation • ECCV 2018 • Matthias Müller, Adel Bibi, Silvio Giancola, Salman Al-Subaihi, Bernard Ghanem
In this work, we present TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild.
no code implementations • 3 Mar 2018 • Guohao Li, Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-to-end policy, which directly predicts controls from raw images.
7 code implementations • 6 Oct 2017 • Felipe Codevilla, Matthias Müller, Antonio López, Vladlen Koltun, Alexey Dosovitskiy
However, driving policies trained via imitation learning cannot be controlled at test time.
no code implementations • 19 Aug 2017 • Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years.
no code implementations • 19 Aug 2017 • Matthias Müller, Vincent Casser, Jean Lahoud, Neil Smith, Bernard Ghanem
We present a photo-realistic training and evaluation simulator (Sim4CV) with extensive applications across various fields of computer vision.