Stereo on a budget

TL;DR

This paper introduces a low-bandwidth stereo depth estimation method where the right camera transmits only a small fraction of its pixels, enabling accurate depth recovery with minimal communication and computational load.

Contribution

The authors propose a novel sparse pixel transmission approach for stereo depth estimation that reduces communication and computation compared to traditional methods.

Findings

Depth maps comparable to traditional stereo algorithms.

Requires only 1+ε images, with ε as low as 2%.

Decoding runtime is linear in image size.

Abstract

We propose an algorithm for recovering depth using less than two images. Instead of having both cameras send their entire image to the host computer, the left camera sends its image to the host while the right camera sends only a fraction $\epsilon$ of its image. The key aspect is that the cameras send the information without communicating at all. Hence, the required communication bandwidth is significantly reduced. While standard image compression techniques can reduce the communication bandwidth, this requires additional computational resources on the part of the encoder (camera). We aim at designing a light weight encoder that only touches a fraction of the pixels. The burden of decoding is placed on the decoder (host). We show that it is enough for the encoder to transmit a sparse set of pixels. Using only $1+\epsilon$ images, with $\epsilon$ as little as 2% of the image, the decoder can compute a depth map. The depth map's accuracy is comparable to traditional stereo matching algorithms that require both images as input. Using the depth map and the left image, the right image can be synthesized. No computations are required at the encoder, and the decoder's runtime is linear in the images' size.