Search Results for author:
Found 28 papers, 18 papers with code
Learning 3D Perception from Others' Predictions
We investigate a new scenario to construct 3D object detectors: learning from the predictions of a nearby unit that is equipped with an accurate detector.
DiffuBox: Refining 3D Object Detection with Point Diffusion
Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving.
Better Monocular 3D Detectors with LiDAR from the Past
Accurate 3D object detection is crucial to autonomous driving.
Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum Learning
Deep reinforcement learning (RL) approaches have been broadly applied to a large number of robotics tasks, such as robot manipulation and autonomous driving.
Pre-Training LiDAR-Based 3D Object Detectors Through Colorization
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train.
Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features
Additionally, we leverage the statistics for a novel self-training process to stabilize the training.
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts.
Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs
In this paper, we investigate the idea of turning sensor inputs (i. e., images) captured in an adverse condition into a benign one (i. e., sunny), upon which the downstream tasks (e. g., semantic segmentation) can attain high accuracy.
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions
Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions.
Learning to Assess Danger from Movies for Cooperative Escape Planning in Hazardous Environments
To address the first challenge, we propose to harness the enormous amount of visual content available in the form of movies and TV shows, and develop a dataset that can represent hazardous environments encountered in the real world.
Learning to Detect Mobile Objects from LiDAR Scans Without Labels
Current 3D object detectors for autonomous driving are almost entirely trained on human-annotated data.
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
Self-driving cars must detect vehicles, pedestrians, and other traffic participants accurately to operate safely.
Orientation-Discriminative Feature Representation for Decentralized Pedestrian Tracking
This paper focuses on the problem of decentralized pedestrian tracking using a sensor network.
Accelerated consensus in multi-agent networks via memory of local averages
There, the DeGroot update on the current states is followed by a linear combination with the previous states.
Detecting and Mapping Trees in Unstructured Environments with a Stereo Camera and Pseudo-Lidar
We present a method for detecting and mapping trees in noisy stereo camera point clouds, using a learned 3-D object detector.
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection
Self-driving cars must detect other vehicles and pedestrians in 3D to plan safe routes and avoid collisions.
Wasserstein Distances for Stereo Disparity Estimation
Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values.
Ranked #2 on
Stereo Depth Estimation
on KITTI2015
(three pixel error metric)
3D Object Detection From Stereo Images
Autonomous Driving
+5
Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
In the domain of autonomous driving, deep learning has substantially improved the 3D object detection accuracy for LiDAR and stereo camera data alike.
End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection
Reliable and accurate 3D object detection is a necessity for safe autonomous driving.
Interactive Natural Language-based Person Search
In this work, we consider the problem of searching people in an unconstrained environment, with natural language descriptions.
Revisiting Meta-Learning as Supervised Learning
Recent years have witnessed an abundance of new publications and approaches on meta-learning.
An Empirical Study of Person Re-Identification with Attributes
Person re-identification aims to identify a person from an image collection, given one image of that person as the query.
LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images
We obtain 2-D segmentation predictions by applying Mask-RCNN to the RGB image, and then link this image to a 3-D lidar point cloud by building a graph of connections among 3-D points and 2-D pixels.
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation.
3D Object Detection From Stereo Images
Autonomous Driving
+2
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
However, in this paper we argue that it is not the quality of the data but its representation that accounts for the majority of the difference.
3D Object Detection From Stereo Images
Autonomous Driving
+2
Anytime Stereo Image Depth Estimation on Mobile Devices
Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints.
Ranked #1 on
Stereo Depth Estimation
on KITTI2012
Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking
We present a data association method for vision-based multiple pedestrian tracking, using deep convolutional features to distinguish between different people based on their appearances.
All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles
A novel probabilistic perception algorithm is presented as a real-time joint solution to data association, object tracking, and object classification for an autonomous ground vehicle in all-weather conditions.
