Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | 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). |