Detection-based object labeling in 3D scenes

Lai, K. Robert; Bo, Liefeng; Ren, Xiaofeng; Fox, Dieter

doi:10.1109/icra.2012.6225316

Cited by 147 publications

(126 citation statements)

References 25 publications

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“…The recent availability of affordable depth sensors has also led to an increase in the use of RGB-D data [21,11,3,30,26] to extract various types of 3D information from scenes such as depth, object pose, and intrinsic images. Our method can be easily modified to apply to this domain.…”

Section: Related Workmentioning

confidence: 99%

FPM: Fine Pose Parts-Based Model with 3D CAD Models

Lim

Khosla

Torralba

2014

Lecture Notes in Computer Science

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Abstract. We introduce a novel approach to the problem of localizing objects in an image and estimating their fine-pose. Given exact CAD models, and a few real training images with aligned models, we propose to leverage the geometric information from CAD models and appearance information from real images to learn a model that can accurately estimate fine pose in real images. Specifically, we propose FPM, a fine pose parts-based model, that combines geometric information in the form of shared 3D parts in deformable part based models, and appearance information in the form of objectness to achieve both fast and accurate fine pose estimation. Our method significantly outperforms current state-ofthe-art algorithms in both accuracy and speed.

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Section: Related Workmentioning

confidence: 99%

FPM: Fine Pose Parts-Based Model with 3D CAD Models

Lim

Khosla

Torralba

2014

Lecture Notes in Computer Science

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“…For example, [18,17] use objects from Google's 3D Warehouse to train an object detection system for 3D point clouds collected by robots navigating through urban and indoor environments. [28] uses 3D CAD models as templates for a sliding window search.…”

Section: Related Workmentioning

confidence: 99%

Learning Hierarchical Semantic Segmentations of LIDAR Data

Dohan

Matejek

Funkhouser

2015

2015 International Conference on 3D Vision

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This paper investigates a method for semantic segmentation of small objects in terrestrial LIDAR scans in urban environments. The core research contribution is a hierarchical segmentation algorithm where potential merges between segments are prioritized by a learned affinity function and constrained to occur only if they achieve a significantly high object classification probability. This approach provides a way to integrate a learned shape-prior (the object classifier) into a search for the best semantic segmentation in a fast and practical algorithm. Experiments with LIDAR scans collected by Google Street View cars throughout ∼100 city blocks of New York City show that the algorithm provides better segmentations and classifications than simple alternatives for cars, vans, traffic lights, and street lights.

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“…The conventional way to approach this problem is to constrain the representation into only one of the modalities while integrating information from the other discarded domain as features. That is, the approach can be 2-D driven [5,[8][9][10][11][12]1], in that reasoning is done in the image while integrating 3-D features, or the approach can be 3-D driven [7,[13][14][15], in that the predictions are made on the 3-D data while integrating 2-D features. These approaches are typically only applicable when the two modalities are in correspondence.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Co-inference for Multi-modal Scene Analysis

Munoz

Bagnell

Hebert

2012

Computer Vision – ECCV 2012

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Abstract. We address the problem of understanding scenes from multiple sources of sensor data (e.g., a camera and a laser scanner) in the case where there is no one-to-one correspondence across modalities (e.g., pixels and 3-D points). This is an important scenario that frequently arises in practice not only when two different types of sensors are used, but also when the sensors are not co-located and have different sampling rates. Previous work has addressed this problem by restricting interpretation to a single representation in one of the domains, with augmented features that attempt to encode the information from the other modalities.Instead, we propose to analyze all modalities simultaneously while propagating information across domains during the inference procedure. In addition to the immediate benefit of generating a complete interpretation in all of the modalities, we demonstrate that this co-inference approach also improves performance over the canonical approach.

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Detection-based object labeling in 3D scenes

Cited by 147 publications

References 25 publications

FPM: Fine Pose Parts-Based Model with 3D CAD Models

FPM: Fine Pose Parts-Based Model with 3D CAD Models

Learning Hierarchical Semantic Segmentations of LIDAR Data

Co-inference for Multi-modal Scene Analysis

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