Detection-based object labeling in 3D scenes
Kevin Lai,Liefeng Bo,Xiaofeng Ren,Dieter Fox +3 more
- pp 1330-1337
TLDR
This work utilizes sliding window detectors trained from object views to assign class probabilities to pixels in every RGB-D frame, and performs efficient inference on a Markov Random Field over the voxels, combining cues from view-based detection and 3D shape, to label the scene.Abstract:
We propose a view-based approach for labeling objects in 3D scenes reconstructed from RGB-D (color+depth) videos. We utilize sliding window detectors trained from object views to assign class probabilities to pixels in every RGB-D frame. These probabilities are projected into the reconstructed 3D scene and integrated using a voxel representation. We perform efficient inference on a Markov Random Field over the voxels, combining cues from view-based detection and 3D shape, to label the scene. Our detection-based approach produces accurate scene labeling on the RGB-D Scenes Dataset and improves the robustness of object detection.read more
Citations
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Proceedings ArticleDOI
Real-time grasp detection using convolutional neural networks
Joseph Redmon,Anelia Angelova +1 more
TL;DR: An accurate, real-time approach to robotic grasp detection based on convolutional neural networks that outperforms state-of-the-art approaches by 14 percentage points and runs at 13 frames per second on a GPU.
Proceedings ArticleDOI
Multimodal deep learning for robust RGB-D object recognition
TL;DR: This paper leverages recent progress on Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture for object recognition that is composed of two separate CNN processing streams - one for each modality - which are consecutively combined with a late fusion network.
Book ChapterDOI
Sliding Shapes for 3D Object Detection in Depth Images
Shuran Song,Jianxiong Xiao +1 more
TL;DR: This paper proposes to use depth maps for object detection and design a 3D detector to overcome the major difficulties for recognition, namely the variations of texture, illumination, shape, viewpoint, clutter, occlusion, self-occlusion and sensor noises.
Posted Content
Real-Time Grasp Detection Using Convolutional Neural Networks
Joseph Redmon,Anelia Angelova +1 more
TL;DR: In this paper, a convolutional neural network (CNN) is used for robotic grasp detection, which performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques.
Proceedings ArticleDOI
Unsupervised feature learning for 3D scene labeling
Kevin Lai,Liefeng Bo,Dieter Fox +2 more
TL;DR: This paper presents an approach for labeling objects in 3D scenes that combines features learned from raw RGB-D images and 3D point clouds directly, without any hand-designed features, to assign an object label to every3D point in the scene.
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