Optimizing Food-to-3D Model Automated Pipeline

Computer Vision & 3D Generative AI Engineer – Automated Food-to-3D Model Pipeline, Project Overview

We are building an automated pipeline that converts user-uploaded photos of food and restaurant dishes into clean, photorealistic 3D assets (.glb / .gltf).

Currently, feeding raw user photos into 3D reconstruction APIs (Tripo3D ) causes high-frequency textures (like rice, grains, or sauces) to turn into distorted, lumpy geometric artifacts ("stones" / "slugs"), while moving cutlery and table backgrounds break multiview alignment.

We are looking for an experienced developer with expertise in Computer Vision pre-processing and Generative 3D APIs to design, build, and optimize an automated end-to-end processing pipeline.

Paid / Trial Milestone: Sample Verification Task
Trial Task Requirement (Images Attached):
Attached to this post are 4 photos of a bowl of fried rice taken from different angles.

To be considered for this project, you must demonstrate how you solve the common reconstruction issues with this specific test case:

Process the provided images to clean the background, isolate the bowl, and eliminate the shifting cutlery.

Generate a preview 3D model (.glb or interactive web viewer link, or short screen recording of the wireframe and textured model).

The Acceptance Criteria: The food inside the bowl must generate as a clean, smooth surface (not a chaotic cluster of bumpy "stones" or melted geometry), with the fried rice texture mapped realistically across it.

The Project will be awarded once you have show me the model that you have generated