SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again
Wadim Kehl,Fabian Manhardt,Federico Tombari,Slobodan Ilic,Nassir Navab +4 more
- pp 1530-1538
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TLDR
In this paper, a novel method for detecting 3D model instances and estimating their 6D pose from RGB data in a single shot is presented, which outperforms state-of-the-art methods that leverage RGBD data on multiple challenging datasets.Abstract:
We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover the full 6D pose space and train on synthetic model data only. Our approach competes or surpasses current state-of-the-art methods that leverage RGBD data on multiple challenging datasets. Furthermore, our method produces these results at around 10Hz, which is many times faster than the related methods. For the sake of reproducibility, we make our trained networks and detection code publicly available.read more
Citations
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Proceedings ArticleDOI
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
TL;DR: DenseFusion as mentioned in this paper proposes a heterogeneous architecture that processes the two complementary data sources individually and uses a novel dense fusion network to extract pixel-wise dense feature embedding, from which the pose is estimated.
Proceedings ArticleDOI
Real-Time Seamless Single Shot 6D Object Pose Prediction
TL;DR: A single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses is proposed, which substantially outperforms other recent CNN-based approaches when they are all used without postprocessing.
Book ChapterDOI
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images
TL;DR: This work proposes a real-time RGB-based pipeline for object detection and 6D pose estimation based on a variant of the Denoising Autoencoder trained on simulated views of a 3D model using Domain Randomization.
Proceedings ArticleDOI
PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet
TL;DR: PointNetLK as mentioned in this paper unrolls PointNet and the Lucas & Kanade (LK) algorithm into a single trainable recurrent deep neural network for point cloud registration.
Proceedings ArticleDOI
PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation
TL;DR: A Pixel-wise Voting Network (PVNet) is introduced to regress pixel-wise vectors pointing to the keypoints and use these vectors to vote for keypoint locations, which creates a flexible representation for localizing occluded or truncated keypoints.
References
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