no code implementations • 16 Apr 2024 • Ashkan Mirzaei, Riccardo de Lutio, Seung Wook Kim, David Acuna, Jonathan Kelly, Sanja Fidler, Igor Gilitschenski, Zan Gojcic
In this work, we propose an approach for 3D scene inpainting -- the task of coherently replacing parts of the reconstructed scene with desired content.
1 code implementation • 25 Mar 2024 • Xunjiang Gu, Guanyu Song, Igor Gilitschenski, Marco Pavone, Boris Ivanovic
High-definition (HD) maps have played an integral role in the development of modern autonomous vehicle (AV) stacks, albeit with high associated labeling and maintenance costs.
no code implementations • 19 Mar 2024 • Vidhi Jain, Maria Attarian, Nikhil J Joshi, Ayzaan Wahid, Danny Driess, Quan Vuong, Pannag R Sanketi, Pierre Sermanet, Stefan Welker, Christine Chan, Igor Gilitschenski, Yonatan Bisk, Debidatta Dwibedi
Given a video demonstration of a manipulation task and current visual observations, Vid2Robot directly produces robot actions.
no code implementations • 9 Mar 2024 • Marcel Hussing, Claas Voelcker, Igor Gilitschenski, Amir-Massoud Farahmand, Eric Eaton
We show that deep reinforcement learning can maintain its ability to learn without resetting network parameters in settings where the number of gradient updates greatly exceeds the number of environment samples.
no code implementations • 7 Feb 2024 • Yash Kant, Ziyi Wu, Michael Vasilkovsky, Guocheng Qian, Jian Ren, Riza Alp Guler, Bernard Ghanem, Sergey Tulyakov, Igor Gilitschenski, Aliaksandr Siarohin
We present SPAD, a novel approach for creating consistent multi-view images from text prompts or single images.
no code implementations • 1 Feb 2024 • Guocheng Qian, Junli Cao, Aliaksandr Siarohin, Yash Kant, Chaoyang Wang, Michael Vasilkovsky, Hsin-Ying Lee, Yuwei Fang, Ivan Skorokhodov, Peiye Zhuang, Igor Gilitschenski, Jian Ren, Bernard Ghanem, Kfir Aberman, Sergey Tulyakov
We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously.
1 code implementation • 29 Nov 2023 • Ziyi Wu, Mathias Gehrig, Qing Lyu, Xudong Liu, Igor Gilitschenski
On 1Mpx, RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels.
no code implementations • 27 Oct 2023 • Tristan Aumentado-Armstrong, Ashkan Mirzaei, Marcus A. Brubaker, Jonathan Kelly, Alex Levinshtein, Konstantinos G. Derpanis, Igor Gilitschenski
The resulting latent-space NeRF can produce novel views with higher quality than standard colour-space NeRFs, as the AE can correct certain visual artifacts, while rendering over three times faster.
no code implementations • 24 Oct 2023 • Yash Kant, Aliaksandr Siarohin, Michael Vasilkovsky, Riza Alp Guler, Jian Ren, Sergey Tulyakov, Igor Gilitschenski
Our approach focuses on maximizing the reuse of visible pixels from the source image.
no code implementations • 17 Aug 2023 • Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker, Jonathan Kelly, Alex Levinshtein, Konstantinos G. Derpanis, Igor Gilitschenski
A field is trained on relevance maps of training views, denoted as the relevance field, defining the 3D region within which modifications should be made.
2 code implementations • NeurIPS 2023 • Boris Ivanovic, Guanyu Song, Igor Gilitschenski, Marco Pavone
The field of trajectory forecasting has grown significantly in recent years, partially owing to the release of numerous large-scale, real-world human trajectory datasets for autonomous vehicles (AVs) and pedestrian motion tracking.
no code implementations • 30 Jun 2023 • Claas A Voelcker, Arash Ahmadian, Romina Abachi, Igor Gilitschenski, Amir-Massoud Farahmand
The idea of decision-aware model learning, that models should be accurate where it matters for decision-making, has gained prominence in model-based reinforcement learning.
1 code implementation • 10 Jun 2023 • Ziyi Wu, Xudong Liu, Igor Gilitschenski
Recent advances in zero-shot and few-shot classification heavily rely on the success of pre-trained vision-language models (VLMs) such as CLIP.
no code implementations • NeurIPS 2023 • Ziyi Wu, Jingyu Hu, Wuyue Lu, Igor Gilitschenski, Animesh Garg
Finally, we demonstrate the scalability of SlotDiffusion to unconstrained real-world datasets such as PASCAL VOC and COCO, when integrated with self-supervised pre-trained image encoders.
no code implementations • ICCV 2023 • Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker, Jonathan Kelly, Alex Levinshtein, Konstantinos G. Derpanis, Igor Gilitschenski
The popularity of Neural Radiance Fields (NeRFs) for view synthesis has led to a desire for NeRF editing tools.
1 code implementation • 14 Apr 2023 • Tianshu Kuai, Akash Karthikeyan, Yash Kant, Ashkan Mirzaei, Igor Gilitschenski
Animating an object in 3D often requires an articulated structure, e. g. a kinematic chain or skeleton of the manipulated object with proper skinning weights, to obtain smooth movements and surface deformations.
no code implementations • CVPR 2023 • Yash Kant, Aliaksandr Siarohin, Riza Alp Guler, Menglei Chai, Jian Ren, Sergey Tulyakov, Igor Gilitschenski
Next, we combine PIN with a differentiable LBS module to build an expressive and end-to-end Invertible Neural Skinning (INS) pipeline.
no code implementations • 17 Jan 2023 • Aniket Agarwal, Alex Zhang, Karthik Narasimhan, Igor Gilitschenski, Vishvak Murahari, Yash Kant
Our human studies indicate that ASAP can align videos and annotations with high fidelity, precision, and speed.
no code implementations • CVPR 2023 • Samarth Sinha, Jason Y. Zhang, Andrea Tagliasacchi, Igor Gilitschenski, David B. Lindell
Camera pose estimation is a key step in standard 3D reconstruction pipelines that operate on a dense set of images of a single object or scene.
no code implementations • CVPR 2023 • Ashkan Mirzaei, Tristan Aumentado-Armstrong, Konstantinos G. Derpanis, Jonathan Kelly, Marcus A. Brubaker, Igor Gilitschenski, Alex Levinshtein
We refer to this task as 3D inpainting.
1 code implementation • 22 Oct 2022 • Tim Seyde, Peter Werner, Wilko Schwarting, Igor Gilitschenski, Martin Riedmiller, Daniela Rus, Markus Wulfmeier
While there has been substantial success for solving continuous control with actor-critic methods, simpler critic-only methods such as Q-learning find limited application in the associated high-dimensional action spaces.
no code implementations • 7 Oct 2022 • Maria Attarian, Advaya Gupta, Ziyi Zhou, Wei Yu, Igor Gilitschenski, Animesh Garg
Cognitive planning is the structural decomposition of complex tasks into a sequence of future behaviors.
no code implementations • 4 Jul 2022 • Ashkan Mirzaei, Yash Kant, Jonathan Kelly, Igor Gilitschenski
In this paper we introduce LaTeRF, a method for extracting an object of interest from a scene given 2D images of the entire scene, known camera poses, a natural language description of the object, and a set of point-labels of object and non-object points in the input images.
1 code implementation • 22 May 2022 • Yash Kant, Arun Ramachandran, Sriram Yenamandra, Igor Gilitschenski, Dhruv Batra, Andrew Szot, Harsh Agrawal
Instead, the agent must learn from and is evaluated against human preferences of which objects belong where in a tidy house.
1 code implementation • 18 May 2022 • Ryan Sander, Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Sertac Karaman, Daniela Rus
Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents.
no code implementations • 23 Nov 2021 • Tsun-Hsuan Wang, Alexander Amini, Wilko Schwarting, Igor Gilitschenski, Sertac Karaman, Daniela Rus
Data-driven simulators promise high data-efficiency for driving policy learning.
no code implementations • 23 Nov 2021 • Alexander Amini, Tsun-Hsuan Wang, Igor Gilitschenski, Wilko Schwarting, Zhijian Liu, Song Han, Sertac Karaman, Daniela Rus
Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios.
no code implementations • NeurIPS 2021 • Tim Seyde, Igor Gilitschenski, Wilko Schwarting, Bartolomeo Stellato, Martin Riedmiller, Markus Wulfmeier, Daniela Rus
Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space.
no code implementations • 5 Oct 2021 • Xin Huang, Guy Rosman, Igor Gilitschenski, Ashkan Jasour, Stephen G. McGill, John J. Leonard, Brian C. Williams
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction.
1 code implementation • 19 Feb 2021 • Wilko Schwarting, Tim Seyde, Igor Gilitschenski, Lucas Liebenwein, Ryan Sander, Sertac Karaman, Daniela Rus
We demonstrate the effectiveness of our algorithm in learning competitive behaviors on a novel multi-agent racing benchmark that requires planning from image observations.
1 code implementation • ICLR 2020 • Igor Gilitschenski, Roshni Sahoo, Wilko Schwarting, Alexander Amini, Sertac Karaman, Daniela Rus
Reasoning about uncertain orientations is one of the core problems in many perception tasks such as object pose estimation or motion estimation.
no code implementations • 14 Dec 2019 • Igor Gilitschenski, Guy Rosman, Arjun Gupta, Sertac Karaman, Daniela Rus
Our main contribution is the concept of learning context maps to improve the prediction task.
2 code implementations • 11 Oct 2019 • Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus
We introduce a pruning algorithm that provably sparsifies the parameters of a trained model in a way that approximately preserves the model's predictive accuracy.
no code implementations • 12 Jul 2018 • Marcin Dymczyk, Igor Gilitschenski, Juan Nieto, Simon Lynen, Bernhard Zeisl, Roland Siegwart
We propose LandmarkBoost - an approach that, in contrast to the conventional 2D-3D matching methods, casts the search problem as a landmark classification task.
no code implementations • ICLR 2019 • Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus
We present an efficient coresets-based neural network compression algorithm that sparsifies the parameters of a trained fully-connected neural network in a manner that provably approximates the network's output.
no code implementations • 28 Dec 2017 • Gerhard Kurz, Igor Gilitschenski, Florian Pfaff, Lukas Drude, Uwe D. Hanebeck, Reinhold Haeb-Umbach, Roland Y. Siegwart
In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation.
1 code implementation • 28 Nov 2017 • Thomas Schneider, Marcin Dymczyk, Marius Fehr, Kevin Egger, Simon Lynen, Igor Gilitschenski, Roland Siegwart
On the other hand, maplab provides the research community with a collection of multisession mapping tools that include map merging, visual-inertial batch optimization, and loop closure.
Robotics