mlsanityData to 7 Oct 2026

An Object-Centered Data Acquisition Method for 3D Gaussian Splatting using Mobile Phones

arXiv 2604.19216 ↗First posted 21 Apr 2026cs.CVcited by 0

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Abstract (arXiv)

Data acquisition through mobile phones remains a challenge for 3D Gaussian Splatting (3DGS). In this work we target the object-centered scenario and enable reliable mobile acquisition by providing on-device capture guidance and recording onboard sensor signals for offline reconstruction. After the calibration step, the device orientations are aligned to a baseline frame to obtain relative poses, and the optical axis of the camera is mapped to an object-centered spherical grid for uniform viewpoint indexing. To curb polar sampling bias, we compute area-weighted spherical coverage in real-time and guide the user's motion accordingly. We compare the proposed method with RealityScan and the free-capture strategy. Our method achieves superior reconstruction quality using fewer input images compared to free capture and RealityScan. Further analysis shows that the proposed method is able to obtain more comprehensive and uniform viewpoint coverage during object-centered acquisition.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 18 references that are in the map. First posted on arXiv 21 Apr 2026.

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References · 7

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  3. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  4. 20234D Gaussian Splatting for Real-Time Dynamic Scene Renderingcited by 1,144
  5. 2021RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputscited by 614
  6. 2017VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimatorcited by 418
  7. 2024Neural Radiance Field-based Visual Rendering: A Comprehensive Reviewcited by 12

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