Data-Driven 3D Voxel Patterns for Object Category Recognition
Matlab C++ C Python Shell Makefile
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.
3rd_party three classes Oct 25, 2015
ACF add pascal3d script Jun 24, 2016
ContextW add pascal 2007 Oct 28, 2015
Exemplar_DPM add pascal 2007 Oct 28, 2015
Geometry add 2d exemplars Jan 21, 2016
KITTI add pascal3d script Jun 24, 2016
PASCAL2007 36 views for pedestrian and cyclist in test Oct 28, 2015
Results add more results Jun 15, 2015
voc-release5 pascal3d car 13 Oct 8, 2014
LICENSE add license Jun 24, 2016 new readme Jun 24, 2016

3DVP: Data-Driven 3D Voxel Patterns for Object Category Recognition

Created by Yu Xiang at CVGL at Stanford University.


We propose a novel object representation, 3D Voxel Pattern (3DVP), that jointly encodes the key properties of objects including appearance, 3D shape, viewpoint, occlusion and truncation. We discover 3DVPs in a data-driven way, and train a bank of specialized detectors for a dictionary of 3DVPs. The 3DVP detectors are capable of detecting objects with specific visibility patterns and transferring the meta-data from the 3DVPs to the detected objects, such as 2D segmentation mask, 3D pose as well as occlusion or truncation boundaries. The transferred meta-data allows us to infer the occlusion relationship among objects, which in turn provides improved object recognition results.


3DVP is released under the MIT License (refer to the LICENSE file for details).

Citing 3DVP

If you find SubCNN useful in your research, please consider citing:

    author = {Xiang, Yu and Choi, Wongun and Lin, Yuanqing and Savarese, Silvio},
    title = {Data-Driven 3D Voxel Patterns for Object Category Recognition},
    booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    pages = {1903--1911},
    year = {2015}

File Organization

  1. Geometry: scripts to voxelize 3D CAD models.

    # voxelize 3D CAD models
  2. KITTI/PASCAL3D: scripts to discover 3DVPs from the KITTI/PASCAL3D+ detection benchmark.

    # create 3D voxel exemplars
    # prepare clustering data for 3DVPs
    # clustering to discover 3DVPs
  3. ACF: ACF detectors for 3DVPs

    # training and testing ACF detectors for 3DVPs
  4. ContextW: occlusion reasoing with 3DVPs

    # greedy occlusion reasoning with 3DVPs