no code implementations • CVPR 2024 • Norman Mu, Jingwei Ji, Zhenpei Yang, Nate Harada, Haotian Tang, Kan Chen, Charles R. Qi, Runzhou Ge, Kratarth Goel, Zoey Yang, Scott Ettinger, Rami Al-Rfou, Dragomir Anguelov, Yin Zhou
This symbolic representation is a high-level abstraction of the real world, which may render the motion prediction model vulnerable to perception errors (e. g., failures in detecting open-vocabulary obstacles) while missing salient information from the scene context (e. g., poor road conditions).
no code implementations • ICCV 2023 • Mahyar Najibi, Jingwei Ji, Yin Zhou, Charles R. Qi, Xinchen Yan, Scott Ettinger, Dragomir Anguelov
Closed-set 3D perception models trained on only a pre-defined set of object categories can be inadequate for safety critical applications such as autonomous driving where new object types can be encountered after deployment.
1 code implementation • CVPR 2023 • Yingwei Li, Charles R. Qi, Yin Zhou, Chenxi Liu, Dragomir Anguelov
The MoDAR modality propagates object information from temporal contexts to a target frame, represented as a set of virtual points, one for each object from a waypoint on a forecasted trajectory.
no code implementations • 7 Apr 2023 • Kan Chen, Runzhou Ge, Hang Qiu, Rami Ai-Rfou, Charles R. Qi, Xuanyu Zhou, Zoey Yang, Scott Ettinger, Pei Sun, Zhaoqi Leng, Mustafa Baniodeh, Ivan Bogun, Weiyue Wang, Mingxing Tan, Dragomir Anguelov
To study the effect of these modular approaches, design new paradigms that mitigate these limitations, and accelerate the development of end-to-end motion forecasting models, we augment the Waymo Open Motion Dataset (WOMD) with large-scale, high-quality, diverse LiDAR data for the motion forecasting task.
no code implementations • CVPR 2023 • Bokui Shen, Xinchen Yan, Charles R. Qi, Mahyar Najibi, Boyang Deng, Leonidas Guibas, Yin Zhou, Dragomir Anguelov
Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving.
1 code implementation • CVPR 2023 • Congyue Deng, Chiyu "Max'' Jiang, Charles R. Qi, Xinchen Yan, Yin Zhou, Leonidas Guibas, Dragomir Anguelov
Formulating single-view reconstruction as an image-conditioned 3D generation problem, we optimize the NeRF representations by minimizing a diffusion loss on its arbitrary view renderings with a pretrained image diffusion model under the input-view constraint.
no code implementations • 15 Oct 2022 • Chiyu Max Jiang, Mahyar Najibi, Charles R. Qi, Yin Zhou, Dragomir Anguelov
Continued improvements in deep learning architectures have steadily advanced the overall performance of 3D object detectors to levels on par with humans for certain tasks and datasets, where the overall performance is mostly driven by common examples.
no code implementations • 14 Oct 2022 • Mahyar Najibi, Jingwei Ji, Yin Zhou, Charles R. Qi, Xinchen Yan, Scott Ettinger, Dragomir Anguelov
Learning-based perception and prediction modules in modern autonomous driving systems typically rely on expensive human annotation and are designed to perceive only a handful of predefined object categories.
no code implementations • 14 Oct 2022 • Minghua Liu, Yin Zhou, Charles R. Qi, Boqing Gong, Hao Su, Dragomir Anguelov
Our method co-designs an efficient labeling process with semi/weakly supervised learning and is applicable to nearly any 3D semantic segmentation backbones.
no code implementations • 10 Oct 2022 • Chenxi Liu, Zhaoqi Leng, Pei Sun, Shuyang Cheng, Charles R. Qi, Yin Zhou, Mingxing Tan, Dragomir Anguelov
Developing neural models that accurately understand objects in 3D point clouds is essential for the success of robotics and autonomous driving.
no code implementations • 8 Jun 2022 • Longlong Jing, Ruichi Yu, Henrik Kretzschmar, Kang Li, Charles R. Qi, Hang Zhao, Alper Ayvaci, Xu Chen, Dillon Cower, Yingwei Li, Yurong You, Han Deng, CongCong Li, Dragomir Anguelov
Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving.
no code implementations • CVPR 2022 • Xuanyu Zhou, Charles R. Qi, Yin Zhou, Dragomir Anguelov
Lidars are depth measuring sensors widely used in autonomous driving and augmented reality.
no code implementations • 11 May 2022 • Mao Ye, Chenxi Liu, Maoqing Yao, Weiyue Wang, Zhaoqi Leng, Charles R. Qi, Dragomir Anguelov
While multi-class 3D detectors are needed in many robotics applications, training them with fully labeled datasets can be expensive in labeling cost.
no code implementations • 22 Dec 2021 • Jingxiao Zheng, Xinwei Shi, Alexander Gorban, Junhua Mao, Yang song, Charles R. Qi, Ting Liu, Visesh Chari, Andre Cornman, Yin Zhou, CongCong Li, Dragomir Anguelov
3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors, including the 3D resolution and range of data, absence of dense depth maps, failure modes for LiDAR, relative location between the camera and LiDAR, and a high bar for estimation accuracy.
no code implementations • NeurIPS 2021 • Boyang Deng, Charles R. Qi, Mahyar Najibi, Thomas Funkhouser, Yin Zhou, Dragomir Anguelov
Given the insight that SDE would benefit from more accurate geometry descriptions, we propose to represent objects as amodal contours, specifically amodal star-shaped polygons, and devise a simple model, StarPoly, to predict such contours.
no code implementations • 29 Sep 2021 • Xuanyu Zhou, Charles R. Qi, Yin Zhou, Dragomir Anguelov
However, most prior work focus on the generic point cloud representation, neglecting the spatial patterns of the points from lidar range images.
no code implementations • ICCV 2021 • Qiangeng Xu, Yin Zhou, Weiyue Wang, Charles R. Qi, Dragomir Anguelov
On the Waymo Open Dataset and KITTI, SPG improves 3D detection results of these two methods across all categories.
Ranked #6 on
3D Object Detection
on KITTI Cars Easy
no code implementations • CVPR 2021 • Charles R. Qi, Yin Zhou, Mahyar Najibi, Pei Sun, Khoa Vo, Boyang Deng, Dragomir Anguelov
While current 3D object recognition research mostly focuses on the real-time, onboard scenario, there are many offboard use cases of perception that are largely under-explored, such as using machines to automatically generate high-quality 3D labels.
no code implementations • ICCV 2021 • Scott Ettinger, Shuyang Cheng, Benjamin Caine, Chenxi Liu, Hang Zhao, Sabeek Pradhan, Yuning Chai, Ben Sapp, Charles R. Qi, Yin Zhou, Zoey Yang, Aurelien Chouard, Pei Sun, Jiquan Ngiam, Vijay Vasudevan, Alexander McCauley, Jonathon Shlens, Dragomir Anguelov
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
2 code implementations • ECCV 2020 • Saining Xie, Jiatao Gu, Demi Guo, Charles R. Qi, Leonidas J. Guibas, Or Litany
To this end, we select a suite of diverse datasets and tasks to measure the effect of unsupervised pre-training on a large source set of 3D scenes.
no code implementations • 20 Jul 2020 • Shubham Tulsiani, Or Litany, Charles R. Qi, He Wang, Leonidas J. Guibas
We present an approach for aggregating a sparse set of views of an object in order to compute a semi-implicit 3D representation in the form of a volumetric feature grid.
1 code implementation • CVPR 2020 • Charles R. Qi, Xinlei Chen, Or Litany, Leonidas J. Guibas
Compared to prior work on multi-modal detection, we explicitly extract both geometric and semantic features from the 2D images.
Ranked #2 on
3D Object Detection
on SUN-RGBD
(using extra training data)
13 code implementations • ICCV 2019 • Charles R. Qi, Or Litany, Kaiming He, Leonidas J. Guibas
Current 3D object detection methods are heavily influenced by 2D detectors.
3D Object Detection
3D Object Detection From Monocular Images
+2
10 code implementations • ICCV 2019 • Hugues Thomas, Charles R. Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, Leonidas J. Guibas
Furthermore, these locations are continuous in space and can be learned by the network.
Ranked #1 on
3D Semantic Segmentation
on DALES
2 code implementations • CVPR 2019 • Chong Xiang, Charles R. Qi, Bo Li
Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions.
no code implementations • ICML 2018 • Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken
The past few years have witnessed growth in the computational requirements for training deep convolutional neural networks.
10 code implementations • CVPR 2019 • Xingyu Liu, Charles R. Qi, Leonidas J. Guibas
In this work, we propose a novel deep neural network named $FlowNet3D$ that learns scene flow from point clouds in an end-to-end fashion.
no code implementations • 14 Feb 2018 • Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken
The past few years have witnessed growth in the computational requirements for training deep convolutional neural networks.
no code implementations • ICLR 2018 • Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken
DeePa is a deep learning framework that explores parallelism in all parallelizable dimensions to accelerate the training process of convolutional neural networks.
67 code implementations • CVPR 2018 • Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su, Leonidas J. Guibas
In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.
Ranked #1 on
Object Localization
on KITTI Pedestrians Easy
66 code implementations • NeurIPS 2017 • Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Ranked #2 on
Semantic Segmentation
on Toronto-3D L002
109 code implementations • CVPR 2017 • Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas
Point cloud is an important type of geometric data structure.
Ranked #1 on
3D Face Reconstruction
on !(()&&!|*|*|
1 code implementation • NeurIPS 2016 • Yangyan Li, Soeren Pirk, Hao Su, Charles R. Qi, Leonidas J. Guibas
Each field probing filter is a set of probing points --- sensors that perceive the space.
Ranked #5 on
3D Object Recognition
on ModelNet40
2 code implementations • CVPR 2016 • Charles R. Qi, Hao Su, Matthias Niessner, Angela Dai, Mengyuan Yan, Leonidas J. Guibas
Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations.
Ranked #3 on
3D Object Recognition
on ModelNet40
4 code implementations • ICCV 2015 • Hao Su, Charles R. Qi, Yangyan Li, Leonidas Guibas
Object viewpoint estimation from 2D images is an essential task in computer vision.