3D Attention-Driven Depth Acquisition for Object Identification

ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2016)


Kai Xu1;2;3           Yifei Shi1           Lintao Zheng1           Junyu Zhang4          Min Liu1           Hui Huang3;4           Hao Su5           Daniel Cohen-Or6           Baoquan Chen2

1HPCL, National University of Defense Technology       2Shandong University      3Shenzhen University       4SIAT        5Stanford University       6Tel-Aviv University


Figure 1: We present an autonomous system for active object identification in an indoor scene (a), with consecutive depth acquisitions. The scene is first roughly scanned, and segmented to generate 3D object proposals. Targeting an object proposal (b), the robot performs multi-view object identification, based on a 3D shape database, driven by a 3D Attention Model. The retrieved 3D models are inserted into the scanned scene (c), replacing the corresponding object scans, thus incrementally constructing a 3D scene model (d).


Abstract 

We address the problem of autonomously exploring unknown objects in a scene by consecutive depth acquisitions. The goal is to reconstruct the scene while online identifying the objects from among a large collection of 3D shapes. Fine-grained shape identification demands a meticulous series of observations attending to varying views and parts of the object of interest. Inspired by the recent success of attention-based models for 2D recognition, we develop a 3D Attention Model that selects the best views to scan from, as well as the most informative regions in each view to focus on, to achieve efficient object recognition. The region-level attention leads to focus-driven features which are quite robust against object occlusion. The attention model, trained with the 3D shape collection, encodes the temporal dependencies among consecutive views with deep recurrent networks. This facilitates order-aware view planning accounting for robot movement cost. In achieving instance identification, the shape collection is organized into a hierarchy,associated with pre-trained hierarchical classifiers. The effectiveness of our method is demonstrated on an autonomous robot (PR) that explores a scene and identifies the objects to construct a 3D scene model.


Reference

[To reference our ALGORITHM, API, CODE or DATA in any publication, please include the bibtex below and a link to this webpage.]

We have released the source code of our MV-RNN, which is built on top of Torch.
Github repository

Overview


Figure 2: Our core contribution is a recurrent 3D attention model (3D-RAM) (d). At each time step, it takes a depth image as input, updates its internal state, performs shape classification and estimates the next-best-view. To make instance-level classification feasible, we organize the shape collection with a classification hierarchy (b). At each node of the hierarchy, a multi-view 3D shape classifier (c) assigns the associated shapes into K overlapping groups (a). Online object identification is guided by the 3D-RAM while traversing down the hierarchy for coarse-to-fine classification (e). The feature extractor and classifier of the corresponding node are employed by the 3D-RAM.



Figure 3: Our recurrent 3D attention model (b) is a two-layer stacked recurrent neural network, with a MV-CNN (a) plugged in. The subnetworks of MV-RNN responsible for feature extraction, feature aggregation and classification are substituted by CNN1, view pooling and CNN2 of MV-CNN, respectively. The corresponding subnetworks are shaded in same color. (b) shows a two-time-step expansion of MV-RNN. Dashed arrows indicate information flow across time steps while solid ones represent that within a time step.


Results

Figure 4: Visualization of identification process and attention (both on acquired depth images and identified shapes) for six real objects.


Figure 5: Online modeling results for two indoor scenes (top and bottom rows). Note the implausible model (circled) retrieved for the rolled-up quilt, without contextual information.


Acknowledgments

We thank the anonymous reviewers for their valuable comments and suggestions. This work was supported in part by NSFC (61572507, 61532003, 61622212, 61522213, 61232011), National 973 Program (2015CB352501, 2014CB360503), Guangdong Science and Technology Program (2014B050502009, 2014TX01X033, 2015A030312015, 2016A050503036) and Shenzhen Innovation Program (JCYJ20151015151249564).


BibTex
@article {xu_siga16,
    title = {3D Attention-Driven Depth Acquisition for Object Identification},
    author = {Kai Xu and Yifei Shi and Lintao Zheng and Junyu Zhang and Min Liu and Hui Huang and Hao Su and Daniel Cohen-Or and Baoquan Chen},
    journal = {ACM Transactions on Graphics (Proc. of SIGGRAPH Asia 2016)},
    volume = {35},
    number = {6},
    pages = {238:1--238:14},
    year = {2016}
}

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