3D ShapeNets: A Deep Representation for Volumetric Shapes

Abstract

3D shape is a crucial but heavily underutilized cue in object recognition, mostly due to the lack of a good generic shape representation. With the recent boost of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is even more urgent to have a useful 3D shape model in an object recognition pipeline. Furthermore, when the recognition has low confidence, it is important to have a fail-safe mode for object recognition systems to intelligently choose the best view to obtain extra observation from another viewpoint, in order to reduce the uncertainty as much as possible. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model naturally supports object recognition from 2.5D depth map, and view planning for object recognition. We construct a large-scale 3D computer graphics dataset to train our model, and conduct extensive experiments to study this new representation.

Paper

Supplementary Materials

Data

ModelNet Benchmark Leaderboard

Please email Shuran Song to add or update your results.

AlgorithmModelNet40
Classification
(Accuracy)
ModelNet40
Retrieval
(mAP)
ModelNet10
Classification
(Accuracy)
ModelNet10
Retrieval
(mAP)
ECC [21]83.2%90.0%
PANORAMA-NN [20]90.7%83.5%91.1%87.4%
MVCNN-MultiRes [19]91.4%
FPNN [18]88.4%
PointNet[17]89.2%
Klokov and Lempitsky[16]91.8% 94.0%
LightNet[15]86.90% 93.39%
Xu and Todorovic[14]81.26% 88.00%
Geometry Image [13]83.9% 51.3%88.4%74.9%
Set-convolution [11]90%
PointNet [12]77.6%
3D-GAN [10]83.3%91.0%
VRN Ensemble [9]95.54%97.14%
ORION [8] 93.8%
FusionNet [7]90.8%93.11%
Pairwise [6]90.7%92.8%
MVCNN [3]90.1%79.5%
GIFT [5] 83.10%81.94% 92.35%91.12%
VoxNet [2]83%92%
DeepPano [4]77.63%76.81%85.45%84.18%
3DShapeNets [1]77%49.2%83.5%68.3%

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[15] A Lightweight 3D Convolutional Neural Network for Real-Time 3D Object Recognition
[16] Roman Klokov, Victor Lempitsky Escape from Cells: Deep Kd-Networks for The Recognition of 3D Point Cloud Models
[17] Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. CVPR 2017.
[18] Yangyan Li, Soeren Pirk, Hao Su, Charles R. Qi, and Leonidas J. Guibas. FPNN: Field Probing Neural Networks for 3D Data. NIPS 2016.
[19] Charles R. Qi, Hao Su, Matthias Niessner, Angela Dai, Mengyuan Yan, and Leonidas J. Guibas.
Volumetric and Multi-View CNNs for Object Classification on 3D Data. CVPR 2016.
[20] K. Sfikas, T. Theoharis and I. Pratikakis.
Exploiting the PANORAMA Representation for Convolutional Neural Network Classification and Retrieval. 3DOR2017.
[21] Martin Simonovsky, Nikos Komodakis
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs.

Source code

Presentation

Acknowledgement

This work is supported by gift funds from Intel Corporation and Project X grant to the Princeton Vision Group, and a hardware donation from NVIDIA Corporation. Z.W. is also partially supported by Hong Kong RGC Fellowship. We thank Thomas Funkhouser, Derek Hoiem, Alexei A. Efros, Andrew Owens, Antonio Torralba, Siddhartha Chaudhuri, and Szymon Rusinkiewicz for valuable discussion.