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LEDGER

Code for Never Look Back: Understanding Persistence in 3D Object Memory from Egocentric Videos

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This is 1 of 147 launches in video generation and vision infrastructure — see how it stacks up on momentum and crowding →

1600 other launches read as similar to this one →

Details

External ID
1408472505
Source
GITHUB
Company
—
Product
LEDGER
Website domain
github.com
Launched
Oct. 7, 2026
Cohort
—
Upvotes
15
Upvotes percentile
0.30
Tags
—

Enrichment

Niche
video generation and vision infrastructure
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
research code for 3d object memory persistence in egocentric videos
Manually corrected
False

Could you build this?

No LEDGER is an academic computer vision research implementation for 3D object persistence memory from egocentric video streams, requiring specialized research knowledge in 3D scene reconstruction and spatial-temporal representations.

What it would actually take: Building this requires expertise in 3D computer vision (SLAM, NeRF/3D Gaussian Splatting, or point cloud processing), deep spatial-temporal transformer models, and PyTorch CUDA extensions trained on specialized egocentric video datasets (like Ego4D) using high-end GPU clusters.

Competitors

Other products that read as similar to this one — 1600 launches clear the similarity bar, closest 8 shown.

Attention rank: #1119 of 1601 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 343 days after the earliest competitor.

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