Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Pirk, Sören"'
Autor:
He, Chengan, Sun, Xin, Shu, Zhixin, Luan, Fujun, Pirk, Sören, Herrera, Jorge Alejandro Amador, Michels, Dominik L., Wang, Tuanfeng Y., Zhang, Meng, Rushmeier, Holly, Zhou, Yi
We present Perm, a learned parametric model of human 3D hair designed to facilitate various hair-related applications. Unlike previous work that jointly models the global hair shape and local strand details, we propose to disentangle them using a PCA
Externí odkaz:
http://arxiv.org/abs/2407.19451
Autor:
Xie, Desai, Bi, Sai, Shu, Zhixin, Zhang, Kai, Xu, Zexiang, Zhou, Yi, Pirk, Sören, Kaufman, Arie, Sun, Xin, Tan, Hao
We present LRM-Zero, a Large Reconstruction Model (LRM) trained entirely on synthesized 3D data, achieving high-quality sparse-view 3D reconstruction. The core of LRM-Zero is our procedural 3D dataset, Zeroverse, which is automatically synthesized fr
Externí odkaz:
http://arxiv.org/abs/2406.09371
Autor:
Maesumi, Arman, Hu, Dylan, Saripalli, Krishi, Kim, Vladimir G., Fisher, Matthew, Pirk, Sören, Ritchie, Daniel
Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different types of noise exist, each produced by a separate algorithm. In this pape
Externí odkaz:
http://arxiv.org/abs/2404.16292
Autor:
Kałużny, Jacek, Schreckenberg, Yannik, Cyganik, Karol, Annighöfer, Peter, Pirk, Sören, Michels, Dominik L., Cieslak, Mikolaj, Assaad-Gerbert, Farhah, Benes, Bedrich, Pałubicki, Wojciech
We introduce LAESI, a Synthetic Leaf Dataset of 100,000 synthetic leaf images on millimeter paper, each with semantic masks and surface area labels. This dataset provides a resource for leaf morphology analysis primarily aimed at beech and oak leaves
Externí odkaz:
http://arxiv.org/abs/2404.00593
Autor:
Cieslak, Mikolaj, Govindarajan, Umabharathi, Garcia, Alejandro, Chandrashekar, Anuradha, Hädrich, Torsten, Mendoza-Drosik, Aleksander, Michels, Dominik L., Pirk, Sören, Fu, Chia-Chun, Pałubicki, Wojciech
We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. This model is capable of simulating distinct growth stages of these plants, diverse soil conditions, and rand
Externí odkaz:
http://arxiv.org/abs/2403.18351
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). LJL simulates a dissipative pr
Externí odkaz:
http://arxiv.org/abs/2402.03287
Autor:
Deng, Junchen, Marri, Samhita, Klein, Jonathan, Pałubicki, Wojtek, Pirk, Sören, Chowdhary, Girish, Michels, Dominik L.
Robotic harvesting has the potential to positively impact agricultural productivity, reduce costs, improve food quality, enhance sustainability, and to address labor shortage. In the rapidly advancing field of agricultural robotics, the necessity of
Externí odkaz:
http://arxiv.org/abs/2402.02570
Autor:
Xie, Desai, Li, Jiahao, Tan, Hao, Sun, Xin, Shu, Zhixin, Zhou, Yi, Bi, Sai, Pirk, Sören, Kaufman, Arie E.
Multi-view diffusion models, obtained by applying Supervised Finetuning (SFT) to text-to-image diffusion models, have driven recent breakthroughs in text-to-3D research. However, due to the limited size and quality of existing 3D datasets, they still
Externí odkaz:
http://arxiv.org/abs/2312.13980
Autor:
Zhu, Zhen, Li, Yijun, Lyu, Weijie, Singh, Krishna Kumar, Shu, Zhixin, Pirk, Soeren, Hoiem, Derek
We investigate how to generate multimodal image outputs, such as RGB, depth, and surface normals, with a single generative model. The challenge is to produce outputs that are realistic, and also consistent with each other. Our solution builds on the
Externí odkaz:
http://arxiv.org/abs/2307.01425
Autor:
Francis, Anthony, Pérez-D'Arpino, Claudia, Li, Chengshu, Xia, Fei, Alahi, Alexandre, Alami, Rachid, Bera, Aniket, Biswas, Abhijat, Biswas, Joydeep, Chandra, Rohan, Chiang, Hao-Tien Lewis, Everett, Michael, Ha, Sehoon, Hart, Justin, How, Jonathan P., Karnan, Haresh, Lee, Tsang-Wei Edward, Manso, Luis J., Mirksy, Reuth, Pirk, Sören, Singamaneni, Phani Teja, Stone, Peter, Taylor, Ada V., Trautman, Peter, Tsoi, Nathan, Vázquez, Marynel, Xiao, Xuesu, Xu, Peng, Yokoyama, Naoki, Toshev, Alexander, Martín-Martín, Roberto
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algori
Externí odkaz:
http://arxiv.org/abs/2306.16740