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most citedInfinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation

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cs.CV2026

SimpleProc: Fully Procedural Synthetic Data from Simple Rules for Multi-View Stereo

Zeyu Ma, Alexander Raistrick, Jia Deng

Generating procedural synthetic data for multi-view stereo (MVS) usually requires writing complex rules to match the realism of curated datasets. We demonstrate that we can generat…

cs.CV2025

Evaluating Robustness of Monocular Depth Estimation with Procedural Scene Perturbations

Jack Nugent, Siyang Wu, Zeyu Ma +7

Recent years have witnessed substantial progress on monocular depth estimation, particularly as measured by the success of large models on standard benchmarks. However, performance…

cs.CV2024

OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integration

Yiming Zuo, Willow Yang, Zeyu Ma +1

Depth completion (DC) aims to predict a dense depth map from an RGB image and a sparse depth map. Existing DC methods generalize poorly to new datasets or unseen sparse depth patte…

cs.CV20241 cited

Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation

Alexander Raistrick, Lingjie Mei, Karhan Kayan +9

We introduce Infinigen Indoors, a Blender-based procedural generator of photorealistic indoor scenes. It builds upon the existing Infinigen system, which focuses on natural scenes,…

cs.CV2023

View-Dependent Octree-based Mesh Extraction in Unbounded Scenes for Procedural Synthetic Data

Zeyu Ma, Alexander Raistrick, Lahav Lipson +1

Procedural synthetic data generation has received increasing attention in computer vision. Procedural signed distance functions (SDFs) are a powerful tool for modeling large-scale…