REPOGEO REPORT · LITE

yashbhalgat/HashNeRF-pytorch

Default branch main · commit 82885e69 · scanned 5/22/2026, 8:43:00 PM

GitHub: 1,035 stars · 107 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

22 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface yashbhalgat/HashNeRF-pytorch, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README's opening to highlight core value proposition

Why:

CURRENT

# HashNeRF-pytorch

### 🌟 Update 🌟 
Get answers to any questions about this repository using this HuggingFace Chatbot.

Instant-NGP recently introduced a Multi-resolution Hash Encoding for neural graphics primitives like NeRFs. The original NVIDIA implementation mainly in C++/CUDA, based on tiny-cuda-nn, can train NeRFs upto 100x faster!

This project is a **pure PyTorch** implementation of Instant-NGP, built with the purpose of enabling AI Researchers to play around and innovate further upon this method.

COPY-PASTE FIX

# HashNeRF-pytorch: Pure PyTorch for Instant-NGP's Multi-resolution Hash Encoding

This project provides a **pure PyTorch implementation** of the multi-resolution hash grid encoding technique, pioneered by Instant-NGP, for significantly faster Neural Radiance Field (NeRF) training and real-time neural graphics primitives. It enables AI researchers to easily experiment and innovate on this method without C++/CUDA dependencies.

### 🌟 Update 🌟 
Get answers to any questions about this repository using this HuggingFace Chatbot.

mediumtopics#2

Add 'pytorch' and 'instant-ngp' to repository topics

Why:

CURRENT

3d-reconstruction, artificial-intelligence, computer-graphics, computer-vision, efficient-training, hashing, machine-learning, nerf, neural-network, real-time-rendering, signed-distance-functions

COPY-PASTE FIX

3d-reconstruction, artificial-intelligence, computer-graphics, computer-vision, efficient-training, hashing, machine-learning, nerf, neural-network, real-time-rendering, signed-distance-functions, pytorch, instant-ngp

lowhomepage#3
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://nvlabs.github.io/instant-ngp/
```

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface yashbhalgat/HashNeRF-pytorch

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

NVlabs/instant-ngp

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

NVlabs/instant-ngp · recommended 1×
nerfstudio-project/nerfstudio · recommended 1×
apchen-nlp/TensoRF · recommended 1×
sarafridov/K-Planes · recommended 1×
sxyu/Plenoxels · recommended 1×

CATEGORY QUERY
How can I significantly speed up neural radiance field training using PyTorch?
you: not recommended
AI recommended (in order):
1. Instant-NGP (NVlabs/instant-ngp)
2. nerfstudio (nerfstudio-project/nerfstudio)
3. TensoRF (apchen-nlp/TensoRF)
4. K-Planes (sarafridov/K-Planes)
5. Plenoxels (sxyu/Plenoxels)
6. CUDA/cuDNN
7. Mixed Precision Training
AI recommended 7 alternatives but never named yashbhalgat/HashNeRF-pytorch. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are PyTorch-based approaches for real-time neural graphics primitives with multi-resolution hash encoding?
you: not recommended
AI recommended (in order):
1. torch-ngp
2. nerfstudio
3. tiny-cuda-nn
4. PyTorch-NGP
AI recommended 4 alternatives but never named yashbhalgat/HashNeRF-pytorch. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of yashbhalgat/HashNeRF-pytorch?
pass
AI did not name yashbhalgat/HashNeRF-pytorch — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts yashbhalgat/HashNeRF-pytorch in production, what risks or prerequisites should they evaluate first?
pass
AI named yashbhalgat/HashNeRF-pytorch explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo yashbhalgat/HashNeRF-pytorch solve, and who is the primary audience?
pass
AI did not name yashbhalgat/HashNeRF-pytorch — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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yashbhalgat/HashNeRF-pytorch — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite