205 citations · 445 across the 11 of their papers we have counts for
16 papers
OpenMask3D: Open-Vocabulary 3D Instance Segmentation
Ayça Takmaz, Elisabetta Fedele, Robert W. Sumner +3
We introduce the task of open-vocabulary 3D instance segmentation. Current approaches for 3D instance segmentation can typically only recognize object categories from a pre-defined…
AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation
Yuanwen Yue, Sabarinath Mahadevan, Jonas Schult +4
During interactive segmentation, a model and a user work together to delineate objects of interest in a 3D point cloud. In an iterative process, the model assigns each data point t…
3D Segmentation of Humans in Point Clouds with Synthetic Data
Ayça Takmaz, Jonas Schult, Irem Kaftan +5
Segmenting humans in 3D indoor scenes has become increasingly important with the rise of human-centered robotics and AR/VR applications. To this end, we propose the task of joint 3…
Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries
Yuanwen Yue, Theodora Kontogianni, Konrad Schindler +1
We address 2D floorplan reconstruction from 3D scans. Existing approaches typically employ heuristically designed multi-stage pipelines. Instead, we formulate floorplan reconstruct…
Mask3D: Mask Transformer for 3D Semantic Instance Segmentation
Jonas Schult, Francis Engelmann, Alexander Hermans +3
Modern 3D semantic instance segmentation approaches predominantly rely on specialized voting mechanisms followed by carefully designed geometric clustering techniques. Building on…
4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation
Lars Kreuzberg, Idil Esen Zulfikar, Sabarinath Mahadevan +2
In this work, we present a new paradigm, called 4D-StOP, to tackle the task of 4D Panoptic LiDAR Segmentation. 4D-StOP first generates spatio-temporal proposals using voting-based…
Mix3D: Out-of-Context Data Augmentation for 3D Scenes
Alexey Nekrasov, Jonas Schult, Or Litany +2
We present Mix3D, a data augmentation technique for segmenting large-scale 3D scenes. Since scene context helps reasoning about object semantics, current works focus on models with…
From Points to Multi-Object 3D Reconstruction
Francis Engelmann, Konstantinos Rematas, Bastian Leibe +1
We propose a method to detect and reconstruct multiple 3D objects from a single RGB image. The key idea is to optimize for detection, alignment and shape jointly over all objects i…
SAMP: Shape and Motion Priors for 4D Vehicle Reconstruction
Francis Engelmann, Jörg Stückler, Bastian Leibe
Inferring the pose and shape of vehicles in 3D from a movable platform still remains a challenging task due to the projective sensing principle of cameras, difficult surface proper…
DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes
Jonas Schult, Francis Engelmann, Theodora Kontogianni +1
We propose DualConvMesh-Nets (DCM-Net) a family of deep hierarchical convolutional networks over 3D geometric data that combines two types of convolutions. The first type, geodesic…
3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation
Francis Engelmann, Martin Bokeloh, Alireza Fathi +2
We present 3D-MPA, a method for instance segmentation on 3D point clouds. Given an input point cloud, we propose an object-centric approach where each point votes for its object ce…
Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds
Francis Engelmann, Theodora Kontogianni, Bastian Leibe
In this work, we propose Dilated Point Convolutions (DPC). In a thorough ablation study, we show that the receptive field size is directly related to the performance of 3D point cl…
3D-BEVIS: Bird's-Eye-View Instance Segmentation
Cathrin Elich, Francis Engelmann, Theodora Kontogianni +1
Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of obj…
Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds
Francis Engelmann, Theodora Kontogianni, Jonas Schult +1
In this paper, we present a deep learning architecture which addresses the problem of 3D semantic segmentation of unstructured point clouds. Compared to previous work, we introduce…
Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds
Francis Engelmann, Theodora Kontogianni, Alexander Hermans +1
Deep learning approaches have made tremendous progress in the field of semantic segmentation over the past few years. However, most current approaches operate in the 2D image space…
Keyframe-Based Visual-Inertial Online SLAM with Relocalization
Anton Kasyanov, Francis Engelmann, Jörg Stückler +1
Complementing images with inertial measurements has become one of the most popular approaches to achieve highly accurate and robust real-time camera pose tracking. In this paper, w…