[V0_FILE]json:file="package.json" isMerged="true" { "name": "my-v0-project", "version": "0.1.0", "private": true, "scripts": { "dev": "next dev", "build": "next build", "start": "next start", "lint": "eslint ." }, "dependencies": { "@hookform/resolvers": "^3.10.0", "@radix-ui/react-accordion": "1.2.2", "@radix-ui/react-alert-dialog": "1.1.4", "@radix-ui/react-aspect-ratio": "1.1.1", "@radix-ui/react-avatar": "1.1.2", "@radix-ui/react-checkbox": "1.1.3", "@radix-ui/react-collapsible": "1.1.2", "@radix-ui/react-context-menu": "2.2.4", "@radix-ui/react-dialog": "1.1.4", "@radix-ui/react-dropdown-menu": "2.1.4", "@radix-ui/react-hover-card": "1.1.4", "@radix-ui/react-label": "2.1.1", "@radix-ui/react-menubar": "1.1.4", "@radix-ui/react-navigation-menu": "1.2.3", "@radix-ui/react-popover": "1.1.4", "@radix-ui/react-progress": "1.1.1", "@radix-ui/react-radio-group": "1.2.2", "@radix-ui/react-scroll-area": "1.2.2", "@radix-ui/react-select": "2.1.4", "@radix-ui/react-separator": "1.1.1", "@radix-ui/react-slider": "1.2.2", "@radix-ui/react-slot": "1.1.1", "@radix-ui/react-switch": "1.1.2", "@radix-ui/react-tabs": "1.1.2", "@radix-ui/react-toast": "1.2.4", "@radix-ui/react-toggle": "1.1.1", "@radix-ui/react-toggle-group": "1.1.1", "@radix-ui/react-tooltip": "1.1.6", "@vercel/analytics": "1.3.1", "autoprefixer": "^10.4.20", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "cmdk": "1.0.4", "date-fns": "4.1.0", "embla-carousel-react": "8.5.1", "input-otp": "1.4.1", "lucide-react": "^0.454.0", "next": "16.0.10", "next-themes": "^0.4.6", "react": "19.2.0", "react-day-picker": "9.8.0", "react-dom": "19.2.0", "react-hook-form": "^7.60.0", "react-resizable-panels": "^2.1.7", "recharts": "2.15.4", "sonner": "^1.7.4", "tailwind-merge": "^3.3.1", "tailwindcss-animate": "^1.0.7", "vaul": "^1.1.2", "zod": "3.25.76" }, "devDependencies": { "@tailwindcss/postcss": "^4.1.9", "@types/node": "^22", "@types/react": "^19", "@types/react-dom": "^19", "postcss": "^8.5", "tailwindcss": "^4.1.9", "tw-animate-css": "1.3.3", "typescript": "^5" } } [V0_FILE]css:file="app/globals.css" isQuickEdit="true" isMerged="true" @import "tailwindcss"; @import "tw-animate-css"; @custom-variant dark (&:is(.dark *)); :root { --background: oklch(0.99 0 0); --foreground: oklch(0.15 0.02 250); --card: oklch(1 0 0); --card-foreground: oklch(0.15 0.02 250); --popover: oklch(1 0 0); --popover-foreground: oklch(0.15 0.02 250); --primary: oklch(0.35 0.15 262); --primary-foreground: oklch(1 0 0); --secondary: oklch(0.97 0.01 250); --secondary-foreground: oklch(0.15 0.02 250); --muted: oklch(0.96 0.005 240); --muted-foreground: oklch(0.48 0.015 250); --accent: oklch(0.68 0.18 35); --accent-foreground: oklch(1 0 0); --destructive: oklch(0.577 0.245 27.325); --destructive-foreground: oklch(0.99 0 0); --border: oklch(0.92 0.005 240); --input: oklch(0.97 0.01 250); --ring: oklch(0.35 0.15 262); --chart-1: oklch(0.35 0.15 262); --chart-2: oklch(0.68 0.18 35); --chart-3: oklch(0.97 0.01 250); --chart-4: oklch(0.48 0.015 250); --chart-5: oklch(0.15 0.02 250); --radius: 0.5rem; --sidebar: oklch(0.985 0 0); --sidebar-foreground: oklch(0.145 0 0); --sidebar-primary: oklch(0.205 0 0); --sidebar-primary-foreground: oklch(0.985 0 0); --sidebar-accent: oklch(0.97 0 0); --sidebar-accent-foreground: oklch(0.205 0 0); --sidebar-border: oklch(0.922 0 0); --sidebar-ring: oklch(0.708 0 0); } .dark { --background: oklch(0.145 0 0); --foreground: oklch(0.985 0 0); --card: oklch(0.145 0 0); --card-foreground: oklch(0.985 0 0); --popover: oklch(0.145 0 0); --popover-foreground: oklch(0.985 0 0); --primary: oklch(0.985 0 0); --primary-foreground: oklch(0.205 0 0); --secondary: oklch(0.269 0 0); --secondary-foreground: oklch(0.985 0 0); --muted: oklch(0.269 0 0); --muted-foreground: oklch(0.708 0 0); --accent: oklch(0.269 0 0); --accent-foreground: oklch(0.985 0 0); --destructive: oklch(0.396 0.141 25.723); --destructive-foreground: oklch(0.637 0.237 25.331); --border: oklch(0.269 0 0); --input: oklch(0.269 0 0); --ring: oklch(0.439 0 0); --chart-1: oklch(0.488 0.243 264.376); --chart-2: oklch(0.696 0.17 162.48); --chart-3: oklch(0.769 0.188 70.08); --chart-4: oklch(0.627 0.265 303.9); --chart-5: oklch(0.645 0.246 16.439); --sidebar: oklch(0.205 0 0); --sidebar-foreground: oklch(0.985 0 0); --sidebar-primary: oklch(0.488 0.243 264.376); --sidebar-primary-foreground: oklch(0.985 0 0); --sidebar-accent: oklch(0.269 0 0); --sidebar-accent-foreground: oklch(0.985 0 0); --sidebar-border: oklch(0.269 0 0); --sidebar-ring: oklch(0.439 0 0); } @theme inline { --font-sans: "Geist", "Geist Fallback"; --font-mono: "Geist Mono", "Geist Mono Fallback"; --color-background: var(--background); --color-foreground: var(--foreground); --color-card: var(--card); --color-card-foreground: var(--card-foreground); --color-popover: var(--popover); --color-popover-foreground: var(--popover-foreground); --color-primary: var(--primary); --color-primary-foreground: var(--primary-foreground); --color-secondary: var(--secondary); --color-secondary-foreground: var(--secondary-foreground); --color-muted: var(--muted); --color-muted-foreground: var(--muted-foreground); --color-accent: var(--accent); --color-accent-foreground: var(--accent-foreground); --color-destructive: var(--destructive); --color-destructive-foreground: var(--destructive-foreground); --color-border: var(--border); --color-input: var(--input); --color-ring: var(--ring); --color-chart-1: var(--chart-1); --color-chart-2: var(--chart-2); --color-chart-3: var(--chart-3); --color-chart-4: var(--chart-4); --color-chart-5: var(--chart-5); --radius-sm: calc(var(--radius) - 4px); --radius-md: calc(var(--radius) - 2px); --radius-lg: var(--radius); --radius-xl: calc(var(--radius) + 4px); --color-sidebar: var(--sidebar); --color-sidebar-foreground: var(--sidebar-foreground); --color-sidebar-primary: var(--sidebar-primary); --color-sidebar-primary-foreground: var(--sidebar-primary-foreground); --color-sidebar-accent: var(--sidebar-accent); --color-sidebar-accent-foreground: var(--sidebar-accent-foreground); --color-sidebar-border: var(--sidebar-border); --color-sidebar-ring: var(--sidebar-ring); } @layer base { * { @apply border-border outline-ring/50; } body { @apply bg-background text-foreground; } } [V0_FILE]typescriptreact:file="app/layout.tsx" isMerged="true" import type React from "react" import type { Metadata } from "next" import { Geist, Geist_Mono } from "next/font/google" import { Analytics } from "@vercel/analytics/next" import "./globals.css" const _geist = Geist({ subsets: ["latin"] }) const _geistMono = Geist_Mono({ subsets: ["latin"] }) export const metadata: Metadata = { title: "Pathfinder Case Study | Scalable 360° Stitching & LiDAR Infrastructure Inspection", description: "Professional case study showcasing geospatial processing and computer vision for synchronized 360° imagery, LiDAR overlays, and browser-based corridor inspection at scale", generator: "v0.app", icons: { icon: [ { url: "/icon-light-32x32.png", media: "(prefers-color-scheme: light)", }, { url: "/icon-dark-32x32.png", media: "(prefers-color-scheme: dark)", }, { url: "/icon.svg", type: "image/svg+xml", }, ], apple: "/apple-icon.png", }, } export default function RootLayout({ children, }: Readonly<{ children: React.ReactNode }>) { return ( {children} ) } [V0_FILE]typescriptreact:file="app/page.tsx" import { Card, CardContent } from "@/components/ui/card" import { Badge } from "@/components/ui/badge" export default function CaseStudyPage() { return (

{/* Header Section */}

{/* HestaBit logo */}

Geospatial Processing and Computer Vision

Scalable 360° stitching and LiDAR-synced infrastructure inspection

{/* Key Metrics Hero */}

Input

360° JPEG imagery with EXIF geo-tags, LiDAR LAS clouds, IMU telemetry, CSV assets, ESRI basemaps

Output

Map-synced stitched video with LiDAR overlays, browser viewer, geo-referenced defect pin-drops

Core Methods

FFmpeg stitching, overlap deduplication, tile segmentation, GPU burst processing, timeline synchronization, Three.js visualization, auto-scaling cloud compute

Deployment

S3 ingestion, Lambda-triggered scaling, short-burst GPU/high-core instances, browser delivery, regional fail-over, lifecycle storage

{/* Problem and Solution Section */}

Problem and Solution

Problem: Scale and Cost

Massive 360° datasets required synchronized corridor inspection, but proprietary tools were costly, lacked LiDAR, and failed at scale.

Solution: Chunked Processing

Uploads were chunked, stitched with FFmpeg, and overlap duplicates removed for clean composites.

Problem: Accuracy & Drift

Early outputs showed duplicated objects and misalignment across imagery, LiDAR, and maps.

Solution: Unified Synchronization

LiDAR was tiled, denoised, and aligned with imagery and ESRI via a shared timeline.

Problem: Slow Delivery

Processing took days and large uploads were unreliable.

Solution: Scalable Rendering

Event-driven auto-scaling enabled minutes-level rendering with near-flat cost, delivered through a unified browser viewer.

{/* Main Content - Technical Deep Dive Section */}

{/* Problem Definition */}

Problem Definition

Inspection-grade synchronization of multi-modal corridor data at extreme scale

Visual task to be solved

Produce seamless 360° video aligned with ESRI while enabling synchronized LiDAR inspection across very large datasets.

Key technical constraints

Millions of frames and heavy overlaps
Different sampling rates across data streams
Multi-day single-thread runtimes
Browser memory limits for point clouds
Secure, reliable upload and recovery

Technical success criteria

Minutes-level processing
Reliable handling of hundreds-GB uploads
Improved duplicate detection via QA
High first-pass success with retry
Limited cost growth despite speed gains

{/* Data Characteristics */}

Data Characteristics

High-volume, multi-sensor corridor capture with real-world edge cases

Data source and scale

Road-survey imagery from 10k frames to multi-million projects, plus LiDAR, telemetry, GIS basemaps, and CSV assets.

Labeling method

Algorithmic duplicate detection refined through visual QA review and operator feedback.

Primary data challenges

Overlap duplicates and repeated coordinates
Huge panoramas needing tiling
Near-identical corridor frames
Unstable field-network uploads
Browser limits for dense point clouds

{/* Technical Approach */}

Technical Approach

Chunk-first, event-driven architecture for synchronized large-scale processing

High-level pipeline design

{"Chunked S3 uploads → metadata and tiling → FFmpeg stitching → redundancy pruning → LiDAR decompression, denoising, tiling → master timeline sync → streamed browser visualization."}

Key design decisions or trade-offs

Short bursts on large compute vs long small-instance runs
Fixed-size chunks for linear scaling
Ordinal alignment where GPS repeats
Prefetching instead of recompute
Object storage as source of truth

Non-obvious constraints handled

Fly-over/under-pass duplication
Memory-safe LiDAR tile streaming
Hash-chain frame integrity
Expiring role-scoped storage
Rapid regional fail-over

{/* Models and Algorithms */}

Models and Algorithms

Classical video processing with targeted detection and spatial methods

Model architectures or families

Similarity correlation, hashing, YOLOv8 symbol detection, Three.js rendering, tiled point-cloud visualization.

Core algorithms or methods

Histogram overlap pruning
Content-aware frame hashing
4k tile segmentation
Voxel-grid LiDAR denoising
Quaternion-based synchronization

Training or optimization strategy

Multithreaded GPU-friendly batching
Priority-based auto-scaling
Cached FFmpeg builds
Human-in-loop QA feedback
Client-side WASM preview processing

{/* Results Section - Enhanced with Visual Elements */}

Evaluation and Results

Minutes-level rendering with strong operational impact

{/* Metrics Used - Left Column */}

Metrics used

Processing time, cost per batch, success rate, duplicate accuracy, viewer latency, decision time.

{/* Quantified Performance - Center Column */}

Quantified performance

~24h to ~5 min

10k images processing time

72h to ~8 min

12k images processing time

~100x throughput

for ~2x spend

97% success

on 500 GB uploads

~60% reduction

in storage cost

~85% faster

quote-to-decision cycle

{/* Baseline Comparison - Full Width */}

Baseline comparison

Replaced costly proprietary tools lacking LiDAR and scale.

Removed manual duplicate correction and fragmented GIS workflows.

Delivered single-link browser inspection with synchronized imagery, LiDAR, and ESRI data.

{/* Footer */}

) }

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