Sliding Shapes for 3D Object Detection in Depth Images
The depth information of RGB-D sensors has greatly simplified some
common challenges in computer vision and enabled breakthroughs for several
tasks. In this paper, we propose 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, selfocclusion
and sensor noises. We take a collection of 3D CAD models and render
each CAD model from hundreds of viewpoints to obtain synthetic depth maps.
For each depth rendering, we extract features from the 3D point cloud and train
an Exemplar-SVM classifier. During testing and hard-negative mining, we slide a
3D detection window in 3D space. Experiment results show that our 3D detector
significantly outperforms the state-of-the-art algorithms for both RGB and RGBD
images, and achieves about x1.7 improvement on average precision compared
to DPM and R-CNN. All source code and data are available online.
Source Code and Data
This file contains percompute features for RMRC 3D object detection dataset
(a subset of NYUv2 dataset more infor can be find here),
and all computer graphic models (.off) used to train the models.
Training and testing split : NYUv2.zip in NYUv2 image index, and RMRC.zip in RMRC index.
Due to page limit, we moved a lot of technical details to this file. This file also contains more results and comparison.