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..
PyNeRF: Pyramidal Neural Radiance Fields
Authors: Haithem Turki, Michael Zollhöfer, Christian Richardt, Deva Ramanan
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We first evaluate Py Ne RF s performance by measuring its reconstruction quality on bounded synthetic (Section 4.2) and unbounded real-world (Section 4.3) scenes. We demonstrate Py Ne RF s generalizability by evaluating it on additional Ne RF backbones (Section 4.4) and then explore the convergence benefits of using multiscale training data in city-scale reconstruction scenarios (Section 4.5). We ablate our design decisions in Section 4.6. |
| Researcher Affiliation | Collaboration | Haithem Turki Carnegie Mellon University EMAIL Michael Zollhöfer Meta Reality Labs Research EMAIL Christian Richardt Meta Reality Labs Research EMAIL Deva Ramanan Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Py Ne RF rendering function |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for PyNeRF, nor does it include a link to a code repository. |
| Open Datasets | Yes | We evaluate Py Ne RF on the Multiscale Blender dataset proposed by Mip-Ne RF along with our own Blender scenes (which we name Blender-A )... We evaluate Py Ne RF s convergence properties on the the Argoverse 2 [35] Sensor dataset (to our knowledge, the largest city-scale dataset publicly available). |
| Dataset Splits | No | We implement Py Ne RF on top of the Nerfstudio library [28] and train on each scene with 8,192 rays per batch by default for 20,000 iterations on the Multiscale Blender and Mip-Ne RF 360 datasets, and 50,000 iterations on the Boat dataset and Blender-A... The resulting training set contains 400 billion rays across 150K video frames. |
| Hardware Specification | Yes | We train on each scene with 8,192 rays per batch by default for 20,000 iterations on the Multiscale Blender and Mip-Ne RF 360 datasets, and 50,000 iterations on the Boat dataset and Blender-A. We train a hierarchy of 8 Py Ne RF levels backed by a single multi-resolution hash table similar to that used by i NGP [20] in Section 4.2 and Section 4.3 before evaluating additional backbones in Section 4.4. We use 4 features per level with a hash table size of 220 by default, which we found to give the best quality-performance trade-off on the A100 GPUs we use in our experiments. |
| Software Dependencies | No | The paper mentions using the "Nerfstudio library [28]" but does not provide specific version numbers for Nerfstudio or other essential software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We implement Py Ne RF on top of the Nerfstudio library [28] and train on each scene with 8,192 rays per batch by default for 20,000 iterations on the Multiscale Blender and Mip-Ne RF 360 datasets, and 50,000 iterations on the Boat dataset and Blender-A. We train a hierarchy of 8 Py Ne RF levels backed by a single multi-resolution hash table similar to that used by i NGP [20] in Section 4.2 and Section 4.3 before evaluating additional backbones in Section 4.4. We use 4 features per level with a hash table size of 220 by default, which we found to give the best quality-performance trade-off on the A100 GPUs we use in our experiments. Each Py Ne RF uses a 64-channel density MLP with one hidden layer followed by a 128-channel color MLP with two hidden layers. We use similar model capacities in our baselines for fairness. We sample rays using an occupancy grid [20] on the Multiscale Blender dataset, and with a proposal network [3] on all others. We use gradient scaling [21] to improve training stability in scenes with that capture content at close distance (Blender-A and Boat). We parameterize unbounded scenes with Mip-Ne RF 360 s contraction method. |