Superpixel Guided Network for Three-Dimensional Stereo Matching

Xiaoqian Ye

Abstract:

Superpixel provides local pixel coherence and respects
object boundary, which is beneficial to stereo matching. Recently,
superpixel cues are introduced into deep stereo networks. These
methods develop a superpixel-based sampling scheme to downsample input color images and upsample output disparity maps.
However, in this way, the image details are inevitably lost in the
downsampling and the upsampling process introduces errors in
the final disparity as well. Besides, this mechanism further limits the possibility of utilizing larger and multi-scale superpixels,
which are important to alleviate the matching ambiguity. To address these problems, a superpixel-guided stereo matching method
(LSG-Stereo) is proposed, which explicitly exploits the feature and
disparity consistency within multi-scale superpixels to improve
disparity estimation. To effectively incorporate superpixel cues into
a stereo matching network, two novel modules are designed, including Superpixel Attention Spatial Pyramid Pooling (SA-SPP) and
Superpixel-Guided Refinement (SGR). The SA-SPP module takes
advantage of the content-aware superpixel pooling to construct
an adaptive spatial pooling pyramid for better feature extraction.
The SGR module explicitly utilizes the disparity consistency over
multi-scale superpixels to further refine the disparity estimation
in details and ill-posed regions. The proposed method is evaluated
on the Scene Flow dataset, KITTI 2012, and KITTI 2015 stereo
benchmarks with comprehensive experiments. Experimental results demonstrate that our method can significantly improve the
accuracy of stereo matching, especially in details, occlusions, and
texture-less regions.