Search Results for author: Charles R. Qi

Found 23 papers, 12 papers with code

Multi-Class 3D Object Detection with Single-Class Supervision

no code implementations11 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.

3D Object Detection

Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving

no code implementations22 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.

3D Human Pose Estimation Autonomous Driving

Revisiting 3D Object Detection From an Egocentric Perspective

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.

3D Object Detection Autonomous Driving

Lidar Range Image Compression with Deep Delta Encoding

no code implementations29 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.

Autonomous Driving Image Compression +2

Offboard 3D Object Detection from Point Cloud Sequences

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.

3D Object Detection 3D Object Recognition +1

PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

1 code implementation 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.

Point Cloud Pre-training Representation Learning +3

Object-Centric Multi-View Aggregation

no code implementations20 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.

Novel View Synthesis Pose Estimation

ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes

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 #1 on 3D Object Detection on SUN-RGBD (using extra training data)

3D Object Detection

Generating 3D Adversarial Point Clouds

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.

3D Shape Classification Autonomous Driving

Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks

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.

FlowNet3D: Learning Scene Flow in 3D Point Clouds

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.

Motion Segmentation

Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks

no code implementations14 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.

Exploring the Hidden Dimension in Accelerating 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.

Volumetric and Multi-View CNNs for Object Classification on 3D Data

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.

3D Object Recognition 3D Point Cloud Classification +1

Cannot find the paper you are looking for? You can Submit a new open access paper.