Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement

TL;DR

This paper introduces a novel method for improving depth measurements directly on sensed images using graph-based filtering, leading to enhanced point cloud quality by addressing noise and quantization effects at the sensing stage.

Contribution

We propose a depth enhancement approach that models sensor noise and quantization, utilizing graph learning and MAP filtering to improve point cloud quality before reconstruction.

Findings

Significantly outperforms recent denoising schemes

Effective modeling of depth sensor noise and quantization

Efficient optimization via accelerated gradient descent

Abstract

A 3D point cloud is typically constructed from depth measurements acquired by sensors at one or more viewpoints. The measurements suffer from both quantization and noise corruption. To improve quality, previous works denoise a point cloud \textit{a posteriori} after projecting the imperfect depth data onto 3D space. Instead, we enhance depth measurements directly on the sensed images \textit{a priori}, before synthesizing a 3D point cloud. By enhancing near the physical sensing process, we tailor our optimization to our depth formation model before subsequent processing steps that obscure measurement errors. Specifically, we model depth formation as a combined process of signal-dependent noise addition and non-uniform log-based quantization. The designed model is validated (with parameters fitted) using collected empirical data from a representative depth sensor. To enhance each pixel row in a depth image, we first encode intra-view similarities between available row pixels as edge weights via feature graph learning. We next establish inter-view similarities with another rectified depth image via viewpoint mapping and sparse linear interpolation. This leads to a maximum a posteriori (MAP) graph filtering objective that is convex and differentiable. We minimize the objective efficiently using accelerated gradient descent (AGD), where the optimal step size is approximated via Gershgorin circle theorem (GCT). Experiments show that our method significantly outperformed recent point cloud denoising schemes and state-of-the-art image denoising schemes in two established point cloud quality metrics.