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Non-Local Spatial Propagation Network for Depth Completion (2007.10042v1)

Published 20 Jul 2020 in cs.CV

Abstract: In this paper, we propose a robust and efficient end-to-end non-local spatial propagation network for depth completion. The proposed network takes RGB and sparse depth images as inputs and estimates non-local neighbors and their affinities of each pixel, as well as an initial depth map with pixel-wise confidences. The initial depth prediction is then iteratively refined by its confidence and non-local spatial propagation procedure based on the predicted non-local neighbors and corresponding affinities. Unlike previous algorithms that utilize fixed-local neighbors, the proposed algorithm effectively avoids irrelevant local neighbors and concentrates on relevant non-local neighbors during propagation. In addition, we introduce a learnable affinity normalization to better learn the affinity combinations compared to conventional methods. The proposed algorithm is inherently robust to the mixed-depth problem on depth boundaries, which is one of the major issues for existing depth estimation/completion algorithms. Experimental results on indoor and outdoor datasets demonstrate that the proposed algorithm is superior to conventional algorithms in terms of depth completion accuracy and robustness to the mixed-depth problem. Our implementation is publicly available on the project page.

Citations (295)

Summary

  • The paper presents a novel NLSPN that dynamically identifies non-local neighbors to enhance depth accuracy, particularly at challenging boundaries.
  • It introduces a learnable affinity normalization mechanism, broadening the range of corrections and ensuring stable iterative spatial propagation.
  • Experimental evaluations demonstrate state-of-the-art performance on indoor and outdoor datasets, significantly improving RMSE and iRMSE metrics.

Understanding Non-Local Spatial Propagation Networks for Depth Completion

The paper presents an advanced methodology for tackling the challenges associated with depth completion, introducing a Non-Local Spatial Propagation Network (NLSPN). This network is specifically designed to enhance the estimation of dense depth maps using sparse input data derived from RGB and sparse depth images. Unlike pre-existing local propagation frameworks, which are constrained by their reliance on fixed-local neighbors, the NLSPN leverages non-local neighbor identification to significantly mitigate local inconsistencies and enhance the accuracy of depth completions, particularly at depth boundaries—a known challenge in depth estimation tasks prone to the mixed-depth problem.

In traditional depth completion approaches, methodologies such as those employing convolutional spatial propagation networks (CSPN) or direct regression models have demonstrated decent performance. However, these methods often yield blurry depth boundaries due to their inability to dynamically identify relevant contextual detail beyond a fixed-radius locality. The proposed NLSPN counteracts this limitation by estimating non-local neighbors and their affinities for each pixel, enabling more contextually aware depth refinement during iterative propagation. The iterative nature of the NLSPN allows for the consistent refinement of initial depth predictions based on confidence assessments, thereby reducing the influence of inaccurate or unreliable initial measurements.

A noteworthy contribution of this paper lies in its implementation of learnable affinity normalization. Traditional affinity computation approaches often suffer from limited representational versatility due to a constrained range enforced by the normalization strategy. The NLSPN introduces a novel, learnable normalization mechanism, which affords a broader range of affinity configurations while simultaneously maintaining the stability required during spatial propagation. This learnable affinity normalization enhances the learning dynamics, facilitating more accurate affinity-based corrections over the course of depth completion tasks.

Experimental results assert the superiority of the NLSPN over conventional frameworks both quantitatively and qualitatively. Evaluations on indoor and outdoor datasets showcase the method's proficiency in producing precise depth estimations, with the network achieving state-of-the-art performance in key metrics, including RMSE and iRMSE. The implementation details highlight the efficiency of employing non-local configurations leading to substantial improvements over fixed-local frameworks, particularly in high-challenge regions characterized by depth discontinuities or fine structural details.

The implications of these enhanced capabilities are significant. On a practical level, applications in areas such as autonomous driving, augmented reality, and robotic navigation stand to benefit from the reliable, high-fidelity depth maps capable of maintaining detail around challenging environmental boundaries. Theoretically, the introduction of a learning-focused, non-local approach sets a precedent for further investigation into dynamic, context-aware propagation techniques across a range of computer vision tasks.

In terms of future exploration, integrating additional sensing modalities or leveraging multi-view information provides a pivotal direction for research, potentially improving robustness and accuracy further in varied environmental conditions. Additionally, adapting the end-to-end learnability to cover broader categories of spatial transformation processes within diverse vision tasks could yield even more expansive advancements in AI-driven perception systems. The NLSPN thus serves as a foundation upon which even greater strides in depth perception and spatial understanding could be built.