Practical overview
When it fits
Use it when you want a PyTorch codebase for NeRF training with multiresolution hash encoding, particularly for synthetic-object experiments or the documented ScanNet workflow.
Who it is for
- AI researchers exploring multiresolution hash encoding in NeRF training.
- Developers training NeRF models on synthetic objects or ScanNet scenes.
Documented uses
- Train a NeRF model using the provided synthetic-object configurations.
- Train a NeRF model on a scene from the ScanNet dataset.
- Experiment with optional total-variation and sparsity-inducing training losses.
Requirements & limits
- The project describes itself as pure PyTorch and built on NeRF-pytorch, while characterizing the original Instant-NGP implementation as mainly C++/CUDA.
- Synthetic-object training has a documented dataset source and example command; ScanNet preparation has separate notes and is described as tricky.
- Optional total-variation loss targets smoother embeddings, while the sparsity-inducing option applies to ray weights.
- The repository identifies its license as MIT.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Train a NeRF model with a synthetic-object configuration
Use the documented command to train a chair model, or select another listed synthetic object configuration.
Before you start
- The HashNeRF-pytorch repository
- The nerf-synthetic dataset downloaded from the project-linked Google Drive
Steps
Download the nerf-synthetic dataset from the Google Drive folder linked in the README.
From the project environment, run the command below.
python run_nerf.py --config configs/chair.txt --finest_res 512 --log2_hashmap_size 19 --lrate 0.01 --lrate_decay 10For an object such as ficus or hotdog, replace
configs/chair.txtwith the correspondingconfigs/{object}.txtconfiguration.
Expected outcome
A training run for the selected synthetic-object NeRF configuration.
Guide limitations
- The README documents the training command and dataset source but does not specify environment installation steps.
- The documented example and alternate configurations cover synthetic objects; ScanNet uses separate setup notes.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- yashbhalgat/HashNeRF-pytorch · Official homepage title · retained in its source language
Related options
NeRF-pytorch — The project says it is built on top of this implementation.