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Found 19 papers, 12 papers with code
Classifying point clouds at the facade-level using geometric features and deep learning networks
3D building models with facade details are playing an important role in many applications now.
Transferring facade labels between point clouds with semantic octrees while considering change detection
Point clouds and high-resolution 3D data have become increasingly important in various fields, including surveying, construction, and virtual reality.
Reconstructing facade details using MLS point clouds and Bag-of-Words approach
We propose a new approach for the reconstruction of 3D fa\c{c}ade details.
MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models
Although highly-detailed LoD3 building models reveal great potential in various applications, they have yet to be available.
Scan2LoD3: Reconstructing semantic 3D building models at LoD3 using ray casting and Bayesian networks
To this end, we leverage laser physics and 3D building model priors to probabilistically identify model conflicts.
TUM-FAÇADE: Reviewing and enriching point cloud benchmarks for façade segmentation
Point clouds are widely regarded as one of the best dataset types for urban mapping purposes.
Combining visibility analysis and deep learning for refinement of semantic 3D building models by conflict classification
The semantic voxels and conflicts are combined in a Bayesian network to classify and delineate fa\c{c}ade openings, which are reconstructed using a 3D model library.
CASSPR: Cross Attention Single Scan Place Recognition
CASSPR uses queries from one branch to try to match structures in the other branch, ensuring that both extract self-contained descriptors of the point cloud (rather than one branch dominating), but using both to inform the output global descriptor of the point cloud.
A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds
Recent works on 3D single object tracking treat the task as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for the tracking.
MODISSA: a multipurpose platform for the prototypical realization of vehicle-related applications using optical sensors
We present the current state of development of the sensor-equipped car MODISSA, with which Fraunhofer IOSB realizes a configurable experimental platform for hardware evaluation and software development in the context of mobile mapping and vehicle-related safety and protection.
Pairwise Point Cloud Registration using Graph Matching and Rotation-invariant Features
Registration is a fundamental but critical task in point cloud processing, which usually depends on finding element correspondence from two point clouds.
ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion
We tackle the problem of object completion from point clouds and propose a novel point cloud completion network employing an Asymmetrical Siamese Feature Matching strategy, termed as ASFM-Net.
GraNet: Global Relation-aware Attentional Network for ALS Point Cloud Classification
We conducted comprehensive experiments on two ALS point cloud datasets to evaluate the performance of our proposed framework.
SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition
We tackle the problem of place recognition from point cloud data and introduce a self-attention and orientation encoding network (SOE-Net) that fully explores the relationship between points and incorporates long-range context into point-wise local descriptors.
Ranked #5 on
3D Place Recognition
on Oxford RobotCar Dataset
(AR@1% metric)
VPC-Net: Completion of 3D Vehicles from MLS Point Clouds
Moreover, a new refiner module is also presented to preserve the vehicle details from inputs and refine the complete outputs with fine-grained information.
Scale-Awareness of Light Field Camera based Visual Odometry
The method is tested based on a versatile dataset consisting of challenging indoor and outdoor sequences and is compared to state-of-the-art monocular and stereo approaches.
A Synchronized Stereo and Plenoptic Visual Odometry Dataset
We present a new dataset to evaluate monocular, stereo, and plenoptic camera based visual odometry algorithms.
Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data
Very High Spatial Resolution (VHSR) large-scale SAR image databases are still an unresolved issue in the Remote Sensing field.
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries.
