1. Depth Aware Inpainting for Novel View Synthesis Jayant Thatte
Depth Aware Inpainting for Novel View Synthesis Jayant Thatte Jean-Baptiste Boin
Motivation: Depth-based novel view synthesis often leads to missing pixels in the output image. Inpainting done without accounting for depth results in incorrect final result. In this work, we explore using depth- aware inpainting to fill these missing regions.

2. Goal and Methodology
Goal: Inpaint the missing regions in the novel views synthesized using depth-based rendering and compare the results with groundtruth
Plan of Action
Use the texture-plus-depth images from Middlebury Stereo Dataset and synthesize novel views
Use 2 different algorithms – Markov Random Fields and convolutional neural networks – to inpaint the missing regions
Evalute with ground truth views (also available in the dataset)

3. Dataset and Initial Results
Middlebury Stereo Dataset (Scharstein 2014)
23 indoor scenes with rectified stereo image + depth (2880 x 1988)
Depth aware inpainting fills missing regions with background texture
Foreground objects bleed into the background when inpainting without depth
Method 1 results in blurry output compared to method 2
Ground truth evaluations before the final report
Input
W/o Depth
Using Depth
Groundtruth
Result: Method 1
Result: Method 2