Search Results for author:
Found 11 papers, 7 papers with code
Mix3D: Out-of-Context Data Augmentation for 3D Scenes
Since scene context helps reasoning about object semantics, current works focus on models with large capacity and receptive fields that can fully capture the global context of an input 3D scene.
Ranked #1 on
Semantic Segmentation
on ScanNet
From Points to Multi-Object 3D Reconstruction
We propose a method to detect and reconstruct multiple 3D objects from a single RGB image.
3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance Segmentation
We show that grouping proposals improves over NMS and outperforms previous state-of-the-art methods on the tasks of 3D object detection and semantic instance segmentation on the ScanNetV2 benchmark and the S3DIS dataset.
SAMP: Shape and Motion Priors for 4D Vehicle Reconstruction
In this paper, we propose to use 3D shape and motion priors to regularize the estimation of the trajectory and the shape of vehicles in sequences of stereo images.
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.
3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation
We show that grouping proposals improves over NMS and outperforms previous state-of-the-art methods on the tasks of 3D object detection and semantic instance segmentation on the ScanNetV2 benchmark and the S3DIS dataset.
Ranked #1 on
3D Semantic Instance Segmentation
on ScanNetV2
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 #15 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
+2
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.
Keyframe-Based Visual-Inertial Online SLAM with Relocalization
Our visual-inertial SLAM system is based on a real-time capable visual-inertial odometry method that provides locally consistent trajectory and map estimates.
