2.3k citations · 5.7k across the 44 of their papers we have counts for
44 papers
Fully Sparse Fusion for 3D Object Detection
Yingyan Li, Lue Fan, Yang Liu +4
Currently prevalent multimodal 3D detection methods are built upon LiDAR-based detectors that usually use dense Bird's-Eye-View (BEV) feature maps. However, the cost of such BEV fe…
3D Video Object Detection with Learnable Object-Centric Global Optimization
Jiawei He, Yuntao Chen, Naiyan Wang +1
We explore long-term temporal visual correspondence-based optimization for 3D video object detection in this work. Visual correspondence refers to one-to-one mappings for pixels ac…
FeatureNeRF: Learning Generalizable NeRFs by Distilling Foundation Models
Jianglong Ye, Naiyan Wang, Xiaolong Wang
Recent works on generalizable NeRFs have shown promising results on novel view synthesis from single or few images. However, such models have rarely been applied on other downstrea…
Learnable Graph Matching: A Practical Paradigm for Data Association
Jiawei He, Zehao Huang, Naiyan Wang +1
Data association is at the core of many computer vision tasks, e.g., multiple object tracking, image matching, and point cloud registration. however, current data association solut…
Deep Planar Parallax for Monocular Depth Estimation
Haoqian Liang, Zhichao Li, Ya Yang +1
Recent research has highlighted the utility of Planar Parallax Geometry in monocular depth estimation. However, its potential has yet to be fully realized because networks rely hea…
Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection
Shaofei Huang, Zhenwei Shen, Zehao Huang +5
Monocular 3D lane detection is a challenging task due to its lack of depth information. A popular solution is to first transform the front-viewed (FV) images or features into the b…
PredNAS: A Universal and Sample Efficient Neural Architecture Search Framework
Liuchun Yuan, Zehao Huang, Naiyan Wang
In this paper, we present a general and effective framework for Neural Architecture Search (NAS), named PredNAS. The motivation is that given a differentiable performance estimatio…
YOLOV: Making Still Image Object Detectors Great at Video Object Detection
Yuheng Shi, Naiyan Wang, Xiaojie Guo
Video object detection (VID) is challenging because of the high variation of object appearance as well as the diverse deterioration in some frames. On the positive side, the detect…
GIFS: Neural Implicit Function for General Shape Representation
Jianglong Ye, Yuntao Chen, Naiyan Wang +1
Recent development of neural implicit function has shown tremendous success on high-quality 3D shape reconstruction. However, most works divide the space into inside and outside of…
Embracing Single Stride 3D Object Detector with Sparse Transformer
Lue Fan, Ziqi Pang, Tianyuan Zhang +5
In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases. Overlooking thi…
Immortal Tracker: Tracklet Never Dies
Qitai Wang, Yuntao Chen, Ziqi Pang +2
Previous online 3D Multi-Object Tracking(3DMOT) methods terminate a tracklet when it is not associated with new detections for a few frames. But if an object just goes dark, like b…
SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking
Ziqi Pang, Zhichao Li, Naiyan Wang
3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and approaches in recent years, especially those under the "tracking-by-detection" paradigm. Despite their pr…
Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild
Jianglong Ye, Yuntao Chen, Naiyan Wang +1
Tracking and reconstructing 3D objects from cluttered scenes are the key components for computer vision, robotics and autonomous driving systems. While recent progress in implicit…
Unsupervised Scale-consistent Depth Learning from Video
Jia-Wang Bian, Huangying Zhan, Naiyan Wang +5
We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time. Our contributions…
Direct Differentiable Augmentation Search
Aoming Liu, Zehao Huang, Zhiwu Huang +1
Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and datasets…
Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking
Jiawei He, Zehao Huang, Naiyan Wang +1
Data association across frames is at the core of Multiple Object Tracking (MOT) task. This problem is usually solved by a traditional graph-based optimization or directly learned v…
LiDAR R-CNN: An Efficient and Universal 3D Object Detector
Zhichao Li, Feng Wang, Naiyan Wang
LiDAR-based 3D detection in point cloud is essential in the perception system of autonomous driving. In this paper, we present LiDAR R-CNN, a second stage detector that can general…
QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection
Chenhongyi Yang, Zehao Huang, Naiyan Wang
While general object detection with deep learning has achieved great success in the past few years, the performance and efficiency of detecting small objects are far from satisfact…
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
Ziqi Pang, Zhichao Li, Naiyan Wang
Estimating the states of surrounding traffic participants stays at the core of autonomous driving. In this paper, we study a novel setting of this problem: model-free single-object…
1st Place Solutions of Waymo Open Dataset Challenge 2020 -- 2D Object Detection Track
Zehao Huang, Zehui Chen, Qiaofei Li +2
In this technical report, we present our solutions of Waymo Open Dataset (WOD) Challenge 2020 - 2D Object Track. We adopt FPN as our basic framework. Cascade RCNN, stacked PAFPN Ne…
Auto-Rectify Network for Unsupervised Indoor Depth Estimation
Jia-Wang Bian, Huangying Zhan, Naiyan Wang +3
Single-View depth estimation using the CNNs trained from unlabelled videos has shown significant promise. However, excellent results have mostly been obtained in street-scene drivi…
UST: Unifying Spatio-Temporal Context for Trajectory Prediction in Autonomous Driving
Hao He, Hengchen Dai, Naiyan Wang
Trajectory prediction has always been a challenging problem for autonomous driving, since it needs to infer the latent intention from the behaviors and interactions from traffic pa…
DMLO: Deep Matching LiDAR Odometry
Zhichao Li, Naiyan Wang
LiDAR odometry is a fundamental task for various areas such as robotics, autonomous driving. This problem is difficult since it requires the systems to be highly robust running in…
Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
Hongkai Zhang, Hong Chang, Bingpeng Ma +2
Although two-stage object detectors have continuously advanced the state-of-the-art performance in recent years, the training process itself is far from crystal. In this work, we f…