mlsanityData to 7 Oct 2026

TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering

arXiv 2303.15060 ↗First posted 27 Mar 2023cs.CVcited by 7

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Abstract (arXiv)

We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses. Our method first introduces an RGBD-aided structure from motion, which can yield filtered depth maps and refines camera poses guided by corresponding depth. Then, we adopt the neural implicit surface reconstruction method, which allows for high-quality mesh and develops a new training process for applying a regularization provided by classical multi-view stereo methods. Moreover, we apply a differentiable rendering to fine-tune incomplete texture maps and generate textures which are perceptually closer to the original scene. Our pipeline can be applied to any common objects in the real world without the need for either in-the-lab environments or accurate mask images. We demonstrate results of captured objects with complex shapes and validate our method numerically against existing 3D reconstruction and texture mapping methods.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 39 of its 76 references that are in the map. First posted on arXiv 27 Mar 2023.

Builds on

39 of 76 references.

References · 39

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27 more
  1. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 643
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  27. 2020Stomach 3D Reconstruction Based on Virtual Chromoendoscopic Image Generationcited by 5

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 4.

7 papers.

  1. 2024MeshGS: Adaptive Mesh-Aligned Gaussian Splatting for High-Quality Renderingcited by 19
  2. 2024AGS-Mesh: Adaptive Gaussian Splatting and Meshing with Geometric Priors for Indoor Room Reconstruction Using Smartphonescited by 10
  3. 2023Learning Effective NeRFs and SDFs Representations with 3D Generative Adversarial Networks for 3D Object Generationcited by 3
  4. 2025IM360: Large-scale Indoor Mapping with 360 Camerascited by 3
  5. 2025Multi-Modal 3D Mesh Reconstruction from Images and Textcited by 1
  6. 2025UAVTwin: Neural Digital Twins for UAVs using Gaussian Splattingcited by 1
  7. 2025CPSL: Representing Volumetric Video via Content-Promoted Scene Layerscited by 0