no code implementations • 23 Feb 2024 • Yichen Xie, Hongge Chen, Gregory P. Meyer, Yong Jae Lee, Eric M. Wolff, Masayoshi Tomizuka, Wei Zhan, Yuning Chai, Xin Huang
Observations from different angles enable the recovery of 3D object states from 2D image inputs if we can identify the same instance in different input frames.
no code implementations • 1 Dec 2023 • Mu Cai, Haotian Liu, Dennis Park, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, 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 • ICCV 2023 • Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu
Although halting a token is a non-differentiable operation, our method allows for differentiable end-to-end learning by leveraging an equivalent differentiable forward-pass.
no code implementations • 3 Jun 2020 • Nemanja Djuric, Henggang Cui, Zhaoen Su, Shangxuan Wu, Huahua Wang, Fang-Chieh Chou, Luisa San Martin, Song Feng, Rui Hu, Yang Xu, Alyssa Dayan, Sidney Zhang, Brian C. Becker, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Carl K. Wellington
One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future.
no code implementations • 21 May 2020 • Ankit Laddha, Shivam Gautam, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Carl K. Wellington
We show that our approach significantly improves motion forecasting performance over the existing state-of-the-art.
no code implementations • 12 Mar 2020 • Gregory P. Meyer, Jake Charland, Shreyash Pandey, Ankit Laddha, Shivam Gautam, Carlos Vallespi-Gonzalez, Carl K. Wellington
In this work, we present LaserFlow, an efficient method for 3D object detection and motion forecasting from LiDAR.
no code implementations • 9 Mar 2020 • Shivam Gautam, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Brian C. Becker
Accurate motion state estimation of Vulnerable Road Users (VRUs), is a critical requirement for autonomous vehicles that navigate in urban environments.
no code implementations • CVPR 2021 • Gregory P. Meyer
As a result, our interpretation provides an intuitive way to identify well-suited hyper-parameters by approximating the amount of noise in the data, which we demonstrate through a case study and experimentation on the Faster R-CNN and RetinaNet object detectors.
1 code implementation • 24 Oct 2019 • Gregory P. Meyer, Niranjan Thakurdesai
The capability to detect objects is a core part of autonomous driving.
no code implementations • 25 Apr 2019 • Gregory P. Meyer, Jake Charland, Darshan Hegde, Ankit Laddha, Carlos Vallespi-Gonzalez
In this paper, we present an extension to LaserNet, an efficient and state-of-the-art LiDAR based 3D object detector.
no code implementations • CVPR 2019 • Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact.
no code implementations • ICCV 2015 • Gregory P. Meyer, Shalini Gupta, Iuri Frosio, Dikpal Reddy, Jan Kautz
We introduce a method for accurate three dimensional head pose estimation using a commodity depth camera.