Search Results for author:
Found 10 papers, 5 papers with code
OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding
This report provides an overview of the challenge hosted at the OpenSUN3D Workshop on Open-Vocabulary 3D Scene Understanding held in conjunction with ICCV 2023.
ControlRoom3D: Room Generation using Semantic Proxy Rooms
Central to our approach is a user-defined 3D semantic proxy room that outlines a rough room layout based on semantic bounding boxes and a textual description of the overall room style.
Mask4Former: Mask Transformer for 4D Panoptic Segmentation
With this intention, we propose Mask4Former for the challenging task of 4D panoptic segmentation of LiDAR point clouds.
Ranked #1 on
4D Panoptic Segmentation
on SemanticKITTI
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.
Point2Vec for Self-Supervised Representation Learning on Point Clouds
Recently, the self-supervised learning framework data2vec has shown inspiring performance for various modalities using a masked student-teacher approach.
Ranked #5 on
3D Point Cloud Classification
on ModelNet40
(using extra training data)
3D Part Segmentation
Few-Shot 3D Point Cloud Classification
+3
3D Segmentation of Humans in Point Clouds with Synthetic Data
We address this challenge and propose a framework for generating training data of synthetic humans interacting with real 3D scenes.
Mask3D: Mask Transformer for 3D Semantic Instance Segmentation
Modern 3D semantic instance segmentation approaches predominantly rely on specialized voting mechanisms followed by carefully designed geometric clustering techniques.
Ranked #2 on
3D Instance Segmentation
on STPLS3D
3D Instance Segmentation
3D Semantic Instance Segmentation
+2
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 #26 on
Semantic Segmentation
on ScanNet
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.
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.
