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Found 30 papers, 3 papers with code
360$^\circ$ from a Single Camera: A Few-Shot Approach for LiDAR Segmentation
The teacher is used to pretrain the LiDAR segmentation student network, prior to optional fine-tuning on 360$^\circ$ data.
Transformer-based Detection of Microorganisms on High-Resolution Petri Dish Images
Many medical or pharmaceutical processes have strict guidelines regarding continuous hygiene monitoring.
Light-Weight Vision Transformer with Parallel Local and Global Self-Attention
In our work, we redesign the powerful state-of-the-art Vision Transformer PLG-ViT to a much more compact and efficient architecture that is suitable for such tasks.
Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring
In order to ensure safe autonomous driving, precise information about the conditions in and around the vehicle must be available.
Detection of Driver Drowsiness by Calculating the Speed of Eye Blinking
We consider a simple real-time detection system for drowsiness merely based on the eye blinking rate derived from the eye aspect ratio.
DVMN: Dense Validity Mask Network for Depth Completion
We evaluate our Dense Validity Mask Network (DVMN) on the KITTI depth completion benchmark and achieve state of the art results.
Ranked #6 on
Depth Completion
on KITTI Depth Completion
PDC: Piecewise Depth Completion utilizing Superpixels
Depth completion from sparse LiDAR and high-resolution RGB data is one of the foundations for autonomous driving techniques.
Autoencoder Based Inter-Vehicle Generalization for In-Cabin Occupant Classification
Common domain shift problem formulations consider the integration of multiple source domains, or the target domain during training.
SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences
Large labeled data sets are one of the essential basics of modern deep learning techniques.
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.
LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB
The evaluation of our LRPD approach was done on the pedestrians from the KITTI benchmark.
DFKI Cabin Simulator: A Test Platform for Visual In-Cabin Monitoring Functions
We present a test platform for visual in-cabin scene analysis and occupant monitoring functions.
SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark
We release SVIRO, a synthetic dataset for sceneries in the passenger compartment of ten different vehicles, in order to analyze machine learning-based approaches for their generalization capacities and reliability when trained on a limited number of variations (e. g. identical backgrounds and textures, few instances per class).
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.
Rapid Light Field Depth Estimation with Semi-Global Matching
Running time of the light field depth estimation algorithms is typically high.
A Compact Light Field Camera for Real-Time Depth Estimation
For the first time, we present a depth camera based on the light field principle, which provides real-time depth information as well as a compact design.
DeLiO: Decoupled LiDAR Odometry
Most LiDAR odometry algorithms estimate the transformation between two consecutive frames by estimating the rotation and translation in an intervening fashion.
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.
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
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.
