no code implementations • 1 Dec 2023 • Mu Cai, Haotian Liu, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, Dennis Park, Yong Jae Lee
Furthermore, we present ViP-Bench, a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions, enabling future research in this domain.
no code implementations • ICCV 2023 • Hongge Chen, Zhao Chen, Gregory P. Meyer, Dennis Park, Carl Vondrick, Ashish Shrivastava, Yuning Chai
We present SHIFT3D, a differentiable pipeline for generating 3D shapes that are structurally plausible yet challenging to 3D object detectors.
no code implementations • 4 Jan 2023 • Sifan Ye, Yixing Wang, Jiaman Li, Dennis Park, C. Karen Liu, Huazhe Xu, Jiajun Wu
Large-scale capture of human motion with diverse, complex scenes, while immensely useful, is often considered prohibitively costly.
Ranked #3 on 3D Semantic Scene Completion on PRO-teXt
2D Semantic Segmentation task 1 (8 classes) 3D Semantic Scene Completion +1
no code implementations • CVPR 2023 • Ruoshi Liu, Sachit Menon, Chengzhi Mao, Dennis Park, Simon Stent, Carl Vondrick
Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow.
no code implementations • 5 Oct 2022 • Dennis Park, Jie Li, Dian Chen, Vitor Guizilini, Adrien Gaidon
Our methods leverage commonly available LiDAR or RGB videos during training time to fine-tune the depth representation, which leads to improved 3D detectors.
no code implementations • 17 Jun 2022 • Ruoshi Liu, Sachit Menon, Chengzhi Mao, Dennis Park, Simon Stent, Carl Vondrick
Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow.
no code implementations • CVPR 2022 • Basile Van Hoorick, Purva Tendulka, Didac Suris, Dennis Park, Simon Stent, Carl Vondrick
For computer vision systems to operate in dynamic situations, they need to be able to represent and reason about object permanence.
no code implementations • ICCV 2021 • Aditya Ganeshan, Alexis Vallet, Yasunori Kudo, Shin-ichi Maeda, Tommi Kerola, Rares Ambrus, Dennis Park, Adrien Gaidon
Deep learning models for semantic segmentation rely on expensive, large-scale, manually annotated datasets.
Ranked #34 on Semantic Segmentation on NYU Depth v2
2 code implementations • ICCV 2021 • Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, Adrien Gaidon
Recent progress in 3D object detection from single images leverages monocular depth estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors.
Ranked #1 on Monocular 3D Object Detection on KITTI Pedestrian Moderate (using extra training data)
no code implementations • CVPR 2013 • Dennis Park, C. L. Zitnick, Deva Ramanan, Piotr Dollar
We describe novel but simple motion features for the problem of detecting objects in video sequences.