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Found 26 papers, 14 papers with code
Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching
Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and generalize poorly to others.
LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection
This setting results in two distinct domain gaps: scenarios and sensors, making it difficult to analyze and evaluate the method accurately.
ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection
Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination.
Semi-supervised 3D Object Detection with Proficient Teachers
To reduce the dependence on large supervision, semi-supervised learning (SSL) based approaches have been proposed.
Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models
Though image classification datasets could provide the backbone networks with rich visual features and discriminative ability, they are incapable of fully pre-training the target model (i. e., backbone+segmentation modules) in an end-to-end manner.
A Representation Separation Perspective to Correspondences-free Unsupervised 3D Point Cloud Registration
Existing correspondences-free methods generally learn the holistic representation of the entire point cloud, which is fragile for partial and noisy point clouds.
End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration
Even though considerable progress has been made in deep learning-based 3D point cloud processing, how to obtain accurate correspondences for robust registration remains a major challenge because existing hard assignment methods cannot deal with outliers naturally.
AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection
For generating the ground truth of 2D/3D keypoints, an automatic model-fitting approach has been proposed by fitting the deformed 3D object model and the object mask in the 2D image.
FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection
Then the segmentation results from different sensors are adaptively fused based on the proposed attention-based semantic fusion module.
LiDAR-Aug: A General Rendering-Based Augmentation Framework for 3D Object Detection
Finally, we verify the proposed framework on the public KITTI dataset with different 3D object detectors.
MapFusion: A General Framework for 3D Object Detection with HDMaps
In this paper, we propose a simple but effective framework - MapFusion to integrate the map information into modern 3D object detector pipelines.
IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image
3D object detection from a single image is an important task in Autonomous Driving (AD), where various approaches have been proposed.
Ranked #19 on
Monocular 3D Object Detection
on KITTI Cars Moderate
DVI: Depth Guided Video Inpainting for Autonomous Driving
To get clear street-view and photo-realistic simulation in autonomous driving, we present an automatic video inpainting algorithm that can remove traffic agents from videos and synthesize missing regions with the guidance of depth/point cloud.
Ranked #1 on
Image Inpainting
on ApolloScape
PerMO: Perceiving More at Once from a Single Image for Autonomous Driving
We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image for autonomous driving.
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching
First, we construct combination volumes on the upper levels of the pyramid and develop a cost volume fusion module to integrate them for initial disparity estimation.
Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution
Second, we propose a new framework for real-world DSR, which consists of four modules : 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner; 2) A channel attention strategy to enhance channels with abundant high-frequency components; 3) A multi-stage fusion module to effectively re-exploit the results in the coarse-to-fine process; and 4) A depth refinement module to improve the depth map by TGV regularization and input loss.
Joint 3D Instance Segmentation and Object Detection for Autonomous Driving
To tackle this problem, we propose a simple but practical detection framework to jointly predict the 3D BBox and instance segmentation.
Ranked #11 on
3D Object Detection
on KITTI Cars Hard
LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention
In this paper, we propose an end-to-end online 3D video object detector that operates on point cloud sequences.
Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem
Conventional absolute camera pose via a Perspective-n-Point (PnP) solver often assumes that the correspondences between 2D image pixels and 3D points are given.
IoU Loss for 2D/3D Object Detection
In 2D/3D object detection task, Intersection-over-Union (IoU) has been widely employed as an evaluation metric to evaluate the performance of different detectors in the testing stage.
Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry
Recovering the absolute metric scale from a monocular camera is a challenging but highly desirable problem for monocular camera-based systems.
ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving
Specifically, we first segment each car with a pre-trained Mask R-CNN, and then regress towards its 3D pose and shape based on a deformable 3D car model with or without using semantic keypoints.
Augmented LiDAR Simulator for Autonomous Driving
Instead, we can simply deploy a vehicle with a LiDAR scanner to sweep the street of interests to obtain the background point cloud, based on which annotated point cloud can be automatically generated.
RealPoint3D: Point Cloud Generation from a Single Image with Complex Background
Then, the image together with the retrieved shape model is fed into the proposed network to generate the fine-grained 3D point cloud.
The ApolloScape Open Dataset for Autonomous Driving and its Application
In this paper, we provide a sensor fusion scheme integrating camera videos, consumer-grade motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robust self-localization and semantic segmentation for autonomous driving.
Pixel-variant Local Homography for Fisheye Stereo Rectification Minimizing Resampling Distortion
First, we prove that there indeed exist enough degrees of freedom to apply pixel-wise local homography for stereo rectification.
