Search Results for author: Charles R. Qi

Found 34 papers, 14 papers with code

Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving

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.

Autonomous Driving Knowledge Distillation

MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences

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.

3D Object Detection Motion Forecasting +1

WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting

no code implementations7 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 Mustafa, 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.

Motion Forecasting

GINA-3D: Learning to Generate Implicit Neural Assets in the Wild

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.

Autonomous Driving Representation Learning

NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors

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.

3D Reconstruction

Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining

no code implementations15 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.

3D Object Detection Active Learning +3

Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving

no code implementations14 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.

Autonomous Driving Trajectory Prediction

LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds

no code implementations14 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.

3D Semantic Segmentation Autonomous Driving +3

LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds

no code implementations10 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.

3D Object Detection Autonomous Driving +2

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 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 +1

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

3 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.

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

3D Object Detection object-detection +1

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

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