Search Results for author:
Found 13 papers, 7 papers with code
Is Continual Learning Ready for Real-world Challenges?
Our paper aims to initiate a paradigm shift, advocating for the adoption of continual learning methods through new experimental protocols that better emulate real-world conditions to facilitate breakthroughs in the field.
Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services.
Towards accurate instance segmentation in large-scale LiDAR point clouds
Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances.
AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation
In an iterative process, the model assigns each data point to an object (or the background), while the user corrects errors in the resulting segmentation and feeds them back into the model.
Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries
Instead, we formulate floorplan reconstruction as a single-stage structured prediction task: find a variable-size set of polygons, which in turn are variable-length sequences of ordered vertices.
Interactive Object Segmentation in 3D Point Clouds
We propose an interactive approach for 3D instance segmentation, where users can iteratively collaborate with a deep learning model to segment objects in a 3D point cloud directly.
DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes
That is, the convolutional kernel weights are mapped to the local surface of a given mesh.
Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections
Our approach enables the adaptation to a particular object and its background, to distributions shifts in a test set, to specific object classes, and even to large domain changes, where the imaging modality changes between training and testing.
Ranked #1 on
Interactive Segmentation
on DRIONS-DB
Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds
In a thorough ablation study, we show that the receptive field size is directly related to the performance of 3D point cloud processing tasks, including semantic segmentation and object classification.
Ranked #43 on
Semantic Segmentation
on S3DIS Area5
3D-BEVIS: Bird's-Eye-View Instance Segmentation
A lot of progress was made in the field of object classification and semantic segmentation.
Ranked #4 on
3D Semantic Instance Segmentation
on ScanNetV2
3D Instance Segmentation
3D Semantic Instance Segmentation
+4
Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds
In this paper, we present a deep learning architecture which addresses the problem of 3D semantic segmentation of unstructured point clouds.
Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds
The recently proposed PointNet architecture presents an interesting step ahead in that it can operate on unstructured point clouds, achieving encouraging segmentation results.
Incremental Object Discovery in Time-Varying Image Collections
Abstract In this paper, we address the problem of object discovery in time-varying, large-scale image collections.
