Search Results for author: Fabian Manhardt

Found 23 papers, 11 papers with code

NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes

no code implementations16 Mar 2023 Marie-Julie Rakotosaona, Fabian Manhardt, Diego Martin Arroyo, Michael Niemeyer, Abhijit Kundu, Federico Tombari

Obtaining 3D meshes from neural radiance fields still remains an open challenge since NeRFs are optimized for view synthesis, not enforcing an accurate underlying geometry on the radiance field.

Novel View Synthesis Surface Reconstruction

TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation

no code implementations25 Dec 2022 Hanzhi Chen, Fabian Manhardt, Nassir Navab, Benjamin Busam

In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images.

6D Pose Estimation using RGB

SPARF: Neural Radiance Fields from Sparse and Noisy Poses

no code implementations21 Nov 2022 Prune Truong, Marie-Julie Rakotosaona, Fabian Manhardt, Federico Tombari

Neural Radiance Field (NeRF) has recently emerged as a powerful representation to synthesize photorealistic novel views.

Novel View Synthesis

OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection

no code implementations2 Nov 2022 Yongzhi Su, Yan Di, Fabian Manhardt, Guangyao Zhai, Jason Rambach, Benjamin Busam, Didier Stricker, Federico Tombari

Despite monocular 3D object detection having recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery, such two-stage methods typically suffer from overfitting and are incapable of explicitly encapsulating the geometric relation between depth and object bounding box.

Monocular 3D Object Detection object-detection

SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimation

no code implementations13 Aug 2022 Ruida Zhang, Yan Di, Fabian Manhardt, Federico Tombari, Xiangyang Ji

In this paper, to handle these shortcomings, we propose an end-to-end trainable network SSP-Pose for category-level pose estimation, which integrates shape priors into a direct pose regression network.

Pose Estimation regression

RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation

1 code implementation30 Jul 2022 Ruida Zhang, Yan Di, Zhiqiang Lou, Fabian Manhardt, Federico Tombari, Xiangyang Ji

Category-level object pose estimation aims to predict the 6D pose as well as the 3D metric size of arbitrary objects from a known set of categories.

Pose Estimation

GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting

2 code implementations CVPR 2022 Yan Di, Ruida Zhang, Zhiqiang Lou, Fabian Manhardt, Xiangyang Ji, Nassir Navab, Federico Tombari

While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications.

 Ranked #1 on 6D Pose Estimation on LineMOD (Mean ADD-S metric)

6D Pose Estimation 6D Pose Estimation using RGB +2

Object-aware Monocular Depth Prediction with Instance Convolutions

1 code implementation2 Dec 2021 Enis Simsar, Evin Pınar Örnek, Fabian Manhardt, Helisa Dhamo, Nassir Navab, Federico Tombari

With the advent of deep learning, estimating depth from a single RGB image has recently received a lot of attention, being capable of empowering many different applications ranging from path planning for robotics to computational cinematography.

Depth Estimation Depth Prediction +1

Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs

1 code implementation ICCV 2021 Helisa Dhamo, Fabian Manhardt, Nassir Navab, Federico Tombari

Scene graphs are representations of a scene, composed of objects (nodes) and inter-object relationships (edges), proven to be particularly suited for this task, as they allow for semantic control on the generated content.

SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation

2 code implementations ICCV 2021 Yan Di, Fabian Manhardt, Gu Wang, Xiangyang Ji, Nassir Navab, Federico Tombari

Directly regressing all 6 degrees-of-freedom (6DoF) for the object pose (e. g. the 3D rotation and translation) in a cluttered environment from a single RGB image is a challenging problem.

6D Pose Estimation 6D Pose Estimation using RGB +1

GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation

1 code implementation CVPR 2021 Gu Wang, Fabian Manhardt, Federico Tombari, Xiangyang Ji

In this work, we perform an in-depth investigation on both direct and indirect methods, and propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner from dense correspondence-based intermediate geometric representations.

6D Pose Estimation 6D Pose Estimation using RGB +1

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