no code implementations • CVPR 2013 • Zhile Ren, Gregory Shakhnarovich
We propose a hierarchical segmentation algorithm that starts with a very fine oversegmentation and gradually merges regions using a cascade of boundary classifiers.
no code implementations • CVPR 2016 • Zhile Ren, Erik B. Sudderth
We develop new representations and algorithms for three-dimensional (3D) object detection and spatial layout prediction in cluttered indoor scenes.
Ranked #25 on 3D Object Detection on SUN-RGBD val
no code implementations • 26 Jul 2017 • Zhile Ren, Deqing Sun, Jan Kautz, Erik B. Sudderth
Given two consecutive frames from a pair of stereo cameras, 3D scene flow methods simultaneously estimate the 3D geometry and motion of the observed scene.
no code implementations • CVPR 2018 • Zhile Ren, Erik B. Sudderth
We develop a 3D object detection algorithm that uses latent support surfaces to capture contextual relationships in indoor scenes.
Ranked #24 on 3D Object Detection on SUN-RGBD val
2 code implementations • 23 Oct 2018 • Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth, Jan Kautz
To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account.
no code implementations • ICCV 2019 • Daeyun Shin, Zhile Ren, Erik B. Sudderth, Charless C. Fowlkes
We tackle the problem of automatically reconstructing a complete 3D model of a scene from a single RGB image.
no code implementations • 9 Apr 2019 • Jianwei Yang, Zhile Ren, Mingze Xu, Xinlei Chen, David Crandall, Devi Parikh, Dhruv Batra
Passive visual systems typically fail to recognize objects in the amodal setting where they are heavily occluded.
no code implementations • 11 Jun 2019 • Zhile Ren, Erik B. Sudderth
We develop new representations and algorithms for three-dimensional (3D) object detection and spatial layout prediction in cluttered indoor scenes.
Ranked #22 on 3D Object Detection on SUN-RGBD val
1 code implementation • NeurIPS 2019 • Jianwei Yang, Zhile Ren, Chuang Gan, Hongyuan Zhu, Devi Parikh
Convolutional neural networks process input data by sending channel-wise feature response maps to subsequent layers.
1 code implementation • 2 Oct 2020 • Vincent Cartillier, Zhile Ren, Neha Jain, Stefan Lee, Irfan Essa, Dhruv Batra
We study the task of semantic mapping - specifically, an embodied agent (a robot or an egocentric AI assistant) is given a tour of a new environment and asked to build an allocentric top-down semantic map ("what is where?")
1 code implementation • CVPR 2022 • Zhenpei Yang, Zhile Ren, Qi Shan, QiXing Huang
Deep learning has made significant impacts on multi-view stereo systems.
no code implementations • 20 Nov 2021 • Apoorva Beedu, Zhile Ren, Varun Agrawal, Irfan Essa
We introduce a simple yet effective algorithm that uses convolutional neural networks to directly estimate object poses from videos.
1 code implementation • CVPR 2022 • Zhenpei Yang, Zhile Ren, Miguel Angel Bautista, Zaiwei Zhang, Qi Shan, QiXing Huang
In this paper, we present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses.
1 code implementation • 21 Jul 2022 • Xiaoming Zhao, Fangchang Ma, David Güera, Zhile Ren, Alexander G. Schwing, Alex Colburn
What is really needed to make an existing 2D GAN 3D-aware?
1 code implementation • CVPR 2023 • Chen Ziwen, Kaushik Patnaik, Shuangfei Zhai, Alvin Wan, Zhile Ren, Alex Schwing, Alex Colburn, Li Fuxin
To achieve this, we propose AutoFocusFormer (AFF), a local-attention transformer image recognition backbone, which performs adaptive downsampling by learning to retain the most important pixels for the task.
Ranked #4 on Instance Segmentation on Cityscapes val
1 code implementation • 17 Jul 2023 • Alvin Wan, Hanxiang Hao, Kaushik Patnaik, Yueyang Xu, Omer Hadad, David Güera, Zhile Ren, Qi Shan
However, for multi-branch segments of a model, channel removal can introduce inference-time memory copies.
no code implementations • 3 Apr 2024 • Fred Hohman, Chaoqun Wang, Jinmook Lee, Jochen Görtler, Dominik Moritz, Jeffrey P Bigham, Zhile Ren, Cecile Foret, Qi Shan, Xiaoyi Zhang
On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences.