no code implementations • 25 Apr 2024 • Arman Maesumi, Dylan Hu, Krishi Saripalli, Vladimir G. Kim, Matthew Fisher, Sören Pirk, Daniel Ritchie
Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation.
no code implementations • 31 Mar 2024 • Jacek Kałużny, Yannik Schreckenberg, Karol Cyganik, Peter Annighöfer, Sören Pirk, Dominik L. Michels, Mikolaj Cieslak, Farhah Assaad-Gerbert, Bedrich Benes, Wojciech Pałubicki
We introduce LAESI, a Synthetic Leaf Dataset of 100, 000 synthetic leaf images on millimeter paper, each with semantic masks and surface area labels.
no code implementations • 27 Mar 2024 • Mikolaj Cieslak, Umabharathi Govindarajan, Alejandro Garcia, Anuradha Chandrashekar, Torsten Hädrich, Aleksander Mendoza-Drosik, Dominik L. Michels, Sören Pirk, Chia-Chun Fu, Wojciech Pałubicki
The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data.
no code implementations • 5 Feb 2024 • Mulun Na, Jonathan Klein, Biao Zhang, Wojtek Pałubicki, Sören Pirk, Dominik L. Michels
We introduce the Lennard-Jones layer (LJL) for the equalization of the density of 2D and 3D point clouds through systematically rearranging points without destroying their overall structure (distribution normalization).
no code implementations • 4 Feb 2024 • Junchen Deng, Samhita Marri, Jonathan Klein, Wojtek Pałubicki, Sören Pirk, Girish Chowdhary, Dominik L. Michels
Robotic harvesting has the potential to positively impact agricultural productivity, reduce costs, improve food quality, enhance sustainability, and to address labor shortage.
no code implementations • 21 Dec 2023 • Desai Xie, Jiahao Li, Hao Tan, Xin Sun, Zhixin Shu, Yi Zhou, Sai Bi, Sören Pirk, Arie E. Kaufman
To this end, we introduce Carve3D, an improved RLFT algorithm coupled with a novel Multi-view Reconstruction Consistency (MRC) metric, to enhance the consistency of multi-view diffusion models.
no code implementations • 29 Jun 2023 • Anthony Francis, Claudia Pérez-D'Arpino, Chengshu Li, Fei Xia, Alexandre Alahi, Rachid Alami, Aniket Bera, Abhijat Biswas, Joydeep Biswas, Rohan Chandra, Hao-Tien Lewis Chiang, Michael Everett, Sehoon Ha, Justin Hart, Jonathan P. How, Haresh Karnan, Tsang-Wei Edward Lee, Luis J. Manso, Reuth Mirksy, Sören Pirk, Phani Teja Singamaneni, Peter Stone, Ada V. Taylor, Peter Trautman, Nathan Tsoi, Marynel Vázquez, Xuesu Xiao, Peng Xu, Naoki Yokoyama, Alexander Toshev, Roberto Martín-Martín
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation.
no code implementations • 9 May 2023 • Xiaochen Zhou, Bosheng Li, Bedrich Benes, Songlin Fei, Sören Pirk
We use a neural network pipeline to train a situated latent space that allows us to locally predict branch growth only based on a single node in the branch graph of a tree model.
no code implementations • 14 Oct 2022 • Alex Zihao Zhu, Vincent Casser, Reza Mahjourian, Henrik Kretzschmar, Sören Pirk
We demonstrate that this formulation encourages the models to learn embeddings that are invariant to viewpoint variations and consistent across sensor modalities.
no code implementations • 19 Sep 2022 • Catie Cuan, Edward Lee, Emre Fisher, Anthony Francis, Leila Takayama, Tingnan Zhang, Alexander Toshev, Sören Pirk
Our experiments indicate that our method is able to successfully interpret complex human gestures and to use them as a signal to generate socially compliant trajectories for navigation tasks.
no code implementations • 12 Aug 2020 • Till Niese, Sören Pirk, Matthias Albrecht, Bedrich Benes, Oliver Deussen
The placement of vegetation plays a central role in the realism of virtual scenes.
no code implementations • 16 Jun 2020 • Jonathan Klein, Sören Pirk, Dominik L. Michels
We present a novel domain adaptation framework that uses morphologic segmentation to translate images from arbitrary input domains (real and synthetic) into a uniform output domain.
no code implementations • 8 Jun 2020 • Sören Pirk, Karol Hausman, Alexander Toshev, Mohi Khansari
We show that complex plans can be carried out when executing the robotic task and the robot can interactively adapt to changes in the environment and recover from failure cases.
no code implementations • 6 Jun 2020 • Han Shao, Tassilo Kugelstadt, Torsten Hädrich, Wojciech Pałubicki, Jan Bender, Sören Pirk, Dominik L. Michels
In this contribution, we introduce a novel method to accelerate iterative solvers for physical systems with graph networks (GNs) by predicting the initial guesses to reduce the number of iterations.
no code implementations • CVPR 2021 • Yao Lu, Sören Pirk, Jan Dlabal, Anthony Brohan, Ankita Pasad, Zhao Chen, Vincent Casser, Anelia Angelova, Ariel Gordon
Many computer vision tasks address the problem of scene understanding and are naturally interrelated e. g. object classification, detection, scene segmentation, depth estimation, etc.
no code implementations • 21 Jun 2019 • Xinchen Yan, Mohi Khansari, Jasmine Hsu, Yuanzheng Gong, Yunfei Bai, Sören Pirk, Honglak Lee
Training a deep network policy for robot manipulation is notoriously costly and time consuming as it depends on collecting a significant amount of real world data.
no code implementations • 10 Jun 2019 • Sören Pirk, Mohi Khansari, Yunfei Bai, Corey Lynch, Pierre Sermanet
We propose a self-supervised approach for learning representations of objects from monocular videos and demonstrate it is particularly useful in situated settings such as robotics.
no code implementations • 27 Sep 2016 • Sören Pirk, Vojtech Krs, Kaimo Hu, Suren Deepak Rajasekaran, Hao Kang, Bedrich Benes, Yusuke Yoshiyasu, Leonidas J. Guibas
We introduce a new general representation for proximal interactions among physical objects that is agnostic to the type of objects or interaction involved.