Search Results for author:
Found 24 papers, 3 papers with code
JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation
Part-aware panoptic segmentation is a problem of computer vision that aims to provide a semantic understanding of the scene at multiple levels of granularity.
Multi-task Fusion for Efficient Panoptic-Part Segmentation
For CPP, the PartPQ of our proposed model with joint fusion surpasses the previous state-of-the-art by 1. 6 and 4. 7 percentage points for all areas and segments with parts, respectively.
Object Permanence in Object Detection Leveraging Temporal Priors at Inference Time
Humans understand this concept at young ages and know that another person is still there, even though it is temporarily occluded.
Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation
In class-incremental semantic segmentation (CISS), deep learning architectures suffer from the critical problems of catastrophic forgetting and semantic background shift.
Ranked #1 on
Overlapped 14-1
on Cityscapes
Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences
In recent years, deep neural networks showed their exceeding capabilities in addressing many computer vision tasks including scene flow prediction.
RMS-FlowNet: Efficient and Robust Multi-Scale Scene Flow Estimation for Large-Scale Point Clouds
The proposed RMS-FlowNet is a novel end-to-end learning-based architecture for accurate and efficient scene flow estimation which can operate on point clouds of high density.
Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching
This paper presents an iterative multi-scale coarse-to-fine refinement (iCFR) framework to bridge this gap by allowing it to adopt any stereo matching network to make it fast, more efficient and scalable while keeping comparable accuracy.
A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions
Motion estimation is one of the core challenges in computer vision.
MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow
Contrary to the ongoing trend in automotive applications towards usage of more diverse and more sensors, this work tries to solve the complex scene flow problem under a monocular camera setup, i. e. using a single sensor.
HPERL: 3D Human Pose Estimation from RGB and LiDAR
In-the-wild human pose estimation has a huge potential for various fields, ranging from animation and action recognition to intention recognition and prediction for autonomous driving.
SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation
Interpolation of sparse pixel information towards a dense target resolution finds its application across multiple disciplines in computer vision.
DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR
In this paper, we present DeepLiDARFlow, a novel deep learning architecture which fuses high level RGB and LiDAR features at multiple scales in a monocular setup to predict dense scene flow.
ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching
Thus, we present ResFPN -- a multi-resolution feature pyramid network with multiple residual skip connections, where at any scale, we leverage the information from higher resolution maps for stronger and better localized features.
LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images
We propose a new approach called LiDAR-Flow to robustly estimate a dense scene flow by fusing a sparse LiDAR with stereo images.
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation
In the last few years, convolutional neural networks (CNNs) have demonstrated increasing success at learning many computer vision tasks including dense estimation problems such as optical flow and stereo matching.
Ranked #2 on
Scene Flow Estimation
on KITTI 2015 Scene Flow Test
An Empirical Evaluation Study on the Training of SDC Features for Dense Pixel Matching
Not only the tuning of hyperparameters, but also the gathering and selection of training data, the design of the loss function, and the construction of training schedules is important to get the most out of a model.
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks
Our network has a very large receptive field and avoids striding layers to maintain spatial resolution.
SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation
State-of-the-art scene flow algorithms pursue the conflicting targets of accuracy, run time, and robustness.
Automated Scene Flow Data Generation for Training and Verification
Scene flow describes the 3D position as well as the 3D motion of each pixel in an image.
Dense Scene Flow from Stereo Disparity and Optical Flow
Scene flow describes 3D motion in a 3D scene.
Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning
Vehicles of higher automation levels require the creation of situation awareness.
FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation
Thus, we propose in this paper FlowFields++, where we combine the accurate matches of Flow Fields with a robust interpolation.
Combining Stereo Disparity and Optical Flow for Basic Scene Flow
Scene flow is a description of real world motion in 3D that contains more information than optical flow.
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
While most scene flow methods use either variational optimization or a strong rigid motion assumption, we show for the first time that scene flow can also be estimated by dense interpolation of sparse matches.
