Paper ID: 2405.07847

SceneFactory: A Workflow-centric and Unified Framework for Incremental Scene Modeling

Yijun Yuan, Michael Bleier, Andreas Nüchter

We present SceneFactory, a workflow-centric and unified framework for incremental scene modeling, that supports conveniently a wide range of applications, such as (unposed and/or uncalibrated) multi-view depth estimation, LiDAR completion, (dense) RGB-D/RGB-L/Mono//Depth-only reconstruction and SLAM. The workflow-centric design uses multiple blocks as the basis for building different production lines. The supported applications, i.e., productions avoid redundancy in their designs. Thus, the focus is on each block itself for independent expansion. To support all input combinations, our implementation consists of four building blocks in SceneFactory: (1) Mono-SLAM, (2) depth estimation, (3) flexion and (4) scene reconstruction. Furthermore, we propose an unposed & uncalibrated multi-view depth estimation model (U2-MVD) to estimate dense geometry. U2-MVD exploits dense bundle adjustment for solving for poses, intrinsics, and inverse depth. Then a semantic-awared ScaleCov step is introduced to complete the multi-view depth. Relying on U2-MVD, SceneFactory both supports user-friendly 3D creation (with just images) and bridges the applications of Dense RGB-D and Dense Mono. For high quality surface and color reconstruction, we propose due-purpose Multi-resolutional Neural Points (DM-NPs) for the first surface accessible Surface Color Field design, where we introduce Improved Point Rasterization (IPR) for point cloud based surface query. We implement and experiment with SceneFactory to demonstrate its broad practicability and high flexibility. Its quality also competes or exceeds the tightly-coupled state of the art approaches in all tasks. We contribute the code to the community (https://jarrome.github.io/).

Submitted: May 13, 2024