# Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors

arXiv: https://arxiv.org/abs/1805.04239 · HTML: https://mlsanity.com/paper/1805.04239/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2018.

## Built on by (most cited first, 3 of 3)

- [DeepFusion: Real-Time Dense 3D Reconstruction for Monocular SLAM using Single-View Depth and Gradient Predictions](https://mlsanity.com/paper/2207.12244/) (2022)
- [HySim: An Efficient Hybrid Similarity Measure for Patch Matching in Image Inpainting](https://mlsanity.com/paper/2403.14292/) (2024)
- [Towards the Probabilistic Fusion of Learned Priors into Standard Pipelines for 3D Reconstruction](https://mlsanity.com/paper/2207.13464/) (2022)

## Abstract (arXiv)

We present ``just-in-time reconstruction" as real-time image-guided inpainting of a map with arbitrary scale and sparsity to generate a fully dense depth map for the image. In particular, our goal is to inpaint a sparse map --- obtained from either a monocular visual SLAM system or a sparse sensor --- using a single-view depth prediction network as a virtual depth sensor. We adopt a fairly standard approach to data fusion, to produce a fused depth map by performing inference over a novel fully-connected Conditional Random Field (CRF) which is parameterized by the input depth maps and their pixel-wise confidence weights. Crucially, we obtain the confidence weights that parameterize the CRF model in a data-dependent manner via Convolutional Neural Networks (CNNs) which are trained to model the conditional depth error distributions given each source of input depth map and the associated RGB image. Our CRF model penalises absolute depth error in its nodes and pairwise scale-invariant depth error in its edges, and the confidence-based fusion minimizes the impact of outlier input depth values on the fused result. We demonstrate the flexibility of our method by real-time inpainting of ORB-SLAM, Kinect, and LIDAR depth maps acquired both indoors and outdoors at arbitrary scale and varied amount of irregular sparsity.