Proceedings ArticleDOI
2D-Driven 3D Object Detection in RGB-D Images
Jean Lahoud,Bernard Ghanem +1 more
- pp 4632-4640
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TLDR
The approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques.Abstract:
In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques. We then use the 3D information to orient, place, and score bounding boxes around objects. We independently estimate the orientation for every object, using previous techniques that utilize normal information. Object locations and sizes in 3D are learned using a multilayer perceptron (MLP). In the final step, we refine our detections based on object class relations within a scene. When compared to state-of-the-art detection methods that operate almost entirely in the sparse 3D domain, extensive experiments on the well-known SUN RGB-D dataset [29] show that our proposed method is much faster (4.1s per image) in detecting 3D objects in RGB-D images and performs better (3 mAP higher) than the state-of-the-art method that is 4.7 times slower and comparably to the method that is two orders of magnitude slower. This work hints at the idea that 2D-driven object detection in 3D should be further explored, especially in cases where the 3D input is sparse.read more
Citations
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Proceedings ArticleDOI
Frustum PointNets for 3D Object Detection from RGB-D Data
TL;DR: This work directly operates on raw point clouds by popping up RGBD scans and leverages both mature 2D object detectors and advanced 3D deep learning for object localization, achieving efficiency as well as high recall for even small objects.
Proceedings ArticleDOI
Joint 3D Proposal Generation and Object Detection from View Aggregation
TL;DR: This work presents AVOD, an Aggregate View Object Detection network for autonomous driving scenarios that uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal network (RPN) and a second stage detector network.
Proceedings ArticleDOI
Deep Hough Voting for 3D Object Detection in Point Clouds
TL;DR: VoteNet as mentioned in this paper proposes an end-to-end 3D object detection network based on a synergy of deep point set networks and Hough voting, which achieves state-of-the-art performance on two large datasets of real 3D scans.
Proceedings ArticleDOI
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
TL;DR: PointFusion as mentioned in this paper is a generic 3D object detection method that leverages both image and 3D point cloud information, which predicts multiple 3D box hypotheses and their confidences using the input 3D points as spatial anchors.
Posted Content
Deep Hough Voting for 3D Object Detection in Point Clouds
TL;DR: This work proposes VoteNet, an end-to-end 3D object detection network based on a synergy of deep point set networks and Hough voting that achieves state-of-the-art 3D detection on two large datasets of real 3D scans, ScanNet and SUN RGB-D with a simple design, compact model size and high efficiency.
References
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