| Title: |
Online 3D Scene Reconstruction Using Neural Object Priors |
| Authors: |
Chabal, Thomas; Chen, Shizhe; Ponce, Jean; Schmid, Cordelia |
| Contributors: |
Models of visual object recognition and scene understanding (WILLOW); Département d'informatique - ENS-PSL (DI-ENS); École normale supérieure - Paris (ENS-PSL); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Centre Inria de Paris; Institut National de Recherche en Informatique et en Automatique (Inria); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS); Center for Data Science NYU (CDS); New York University New York (NYU); NYU System (NYU)-NYU System (NYU); Courant Institute of Mathematical Sciences New York (CIMS); It was funded in part by the French government under management of Agence Nationale de la Recherche as part of the “Investissements d’avenir” program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute). JP was supported in part by the Louis Vuitton/ENS chair in artificial intelligenceand a Global Distinguished Professorship at the Courant Institute of Mathematical Sciences and the Center for Data Science at New York University; ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019) |
| Source: |
3DV 2025 - 12th International Conference on 3D Vision 2025; https://hal.science/hal-04888719; 3DV 2025 - 12th International Conference on 3D Vision 2025, Mar 2025, Singapore, Singapore |
| Publisher Information: |
CCSD |
| Publication Year: |
2025 |
| Subject Terms: |
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]; [INFO]Computer Science [cs] |
| Subject Geographic: |
Singapore |
| Description: |
International audience ; This paper addresses the problem of reconstructing a scene online at the level of objects given an RGB-D video sequence. While current object-aware neural implicit representations hold promise, they are limited in online reconstruction efficiency and shape completion. Our main contributions to alleviate the above limitations are twofold. First, we propose a feature grid interpolation mechanism to continuously update grid-based object-centric neural implicit representations as new object parts are revealed. Second, we construct an object library with previously mapped objects in advance and leverage the corresponding shape priors to initialize geometric object models in new videos, subsequently completing them with novel views as well as synthesized past views to avoid losing original object details. Extensive experiments on synthetic environments from the Replica dataset, real-world ScanNet sequences and videos captured in our laboratory demonstrate that our approach outperforms state-of-the-art neural implicit models for this task in terms of reconstruction accuracy and completeness. |
| Document Type: |
conference object |
| Language: |
English |
| Availability: |
https://hal.science/hal-04888719; https://hal.science/hal-04888719v1/document; https://hal.science/hal-04888719v1/file/3DV_2025_Online_Scene_Reconstruction-CameraReady.pdf |
| Rights: |
http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.6D2277AB |
| Database: |
BASE |