SDformer: Efficient End-to-End Transformer for Depth Completion

TL;DR

SDformer introduces a window-based Transformer architecture for depth completion, effectively capturing long-range dependencies with lower computational costs, outperforming CNN-based models on standard datasets.

Contribution

The paper proposes a novel Sparse-to-Dense Transformer (SDformer) with windowed self-attention for efficient depth completion, addressing CNN limitations.

Findings

Achieves state-of-the-art results on NYU Depth V2 and KITTI datasets.

Reduces computational load and parameters compared to CNN models.

Effectively captures long-range dependencies with window-based self-attention.

Abstract

Depth completion aims to predict dense depth maps with sparse depth measurements from a depth sensor. Currently, Convolutional Neural Network (CNN) based models are the most popular methods applied to depth completion tasks. However, despite the excellent high-end performance, they suffer from a limited representation area. To overcome the drawbacks of CNNs, a more effective and powerful method has been presented: the Transformer, which is an adaptive self-attention setting sequence-to-sequence model. While the standard Transformer quadratically increases the computational cost from the key-query dot-product of input resolution which improperly employs depth completion tasks. In this work, we propose a different window-based Transformer architecture for depth completion tasks named Sparse-to-Dense Transformer (SDformer). The network consists of an input module for the depth map and RGB image features extraction and concatenation, a U-shaped encoder-decoder Transformer for extracting deep features, and a refinement module. Specifically, we first concatenate the depth map features with the RGB image features through the input model. Then, instead of calculating self-attention with the whole feature maps, we apply different window sizes to extract the long-range depth dependencies. Finally, we refine the predicted features from the input module and the U-shaped encoder-decoder Transformer module to get the enriching depth features and employ a convolution layer to obtain the dense depth map. In practice, the SDformer obtains state-of-the-art results against the CNN-based depth completion models with lower computing loads and parameters on the NYU Depth V2 and KITTI DC datasets.