Retri3D: 3D Neural Graphics Representation Retrieval
Authors: Yushi Guan, Daniel Kwan, Jean Dandurand, Xi Yan, Ruofan Liang, Yuxuan Zhang, Nilesh Jain, Nilesh Ahuja, Selvakumar Panneer, Nandita Vijaykumar
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that Retri3D is compatible with any NGR representation. On the LERF and Scan Net++ datasets, we show significant improvement in retrieval accuracy compared to existing techniques, while being orders of magnitude faster and storage efficient. [...] 4 EXPERIMENTS AND RESULTS |
| Researcher Affiliation | Collaboration | Yushi Guan1, Daniel Kwan1, Jean Sebastien Dandurand1, Xi Yan1, Ruofan Liang1, Yuxuan Zhang1, Nandita Vijaykumar1 [...] Nilesh Jain2, Nilesh Ahuja2, Selvakumar Panneer2 [...] 1 University of Toronto 2 Intel |
| Pseudocode | No | The paper describes methods through narrative text and figures but does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: "We adapt LERF and Lang Splat methods for scene retrieval because they are open-sourced", referring to baseline methods used, not the authors' own code for Retri3D. There is no explicit statement or link indicating that the source code for Retri3D is released or provided. |
| Open Datasets | Yes | We demonstrate the effectiveness of Retri3D using the LERF and Scan Net++ datasets (Kerr et al., 2023; Yeshwanth et al., 2023). |
| Dataset Splits | No | The paper mentions using 13 scenes from LERF and 280 scenes from Scan Net++ for evaluation. It describes metrics like P@k for retrieval accuracy, which implies evaluating against the entire set of available scenes for a query. However, it does not specify explicit training/validation/test splits for the Retri3D retrieval model itself. |
| Hardware Specification | Yes | Experiments are conducted on a desktop with an Intel i7-13700K CPU, Nvidia RTX 4090 GPU, and 64GB of RAM. We use Py Torch 2.1.2 with CUDA 12.0 on Ubuntu 22.04 LTS. |
| Software Dependencies | Yes | We use Py Torch 2.1.2 with CUDA 12.0 on Ubuntu 22.04 LTS. |
| Experiment Setup | Yes | All models are trained using the default configuration for 30,000 epochs. For Nerfacto, we also enable the "use-gradient-scaling" option to scale the gradient near the camera, reducing artifacts and creating a stronger baseline for rendering from random poses (Philip & Deschaintre, 2023). |