You will get A clean and accurate 3D reconstruction from your images
Project details
You will get a carefully implemented neural implicit 3D reconstruction pipeline designed for research, experimentation, and proof-of-concept use cases. This project is based on my Bachelor’s Thesis work, where I reproduced and stabilized a Pose-Optimized Multi-view Neural Implicit (PMNI) framework for reconstructing 3D geometry from multi-view images.
What sets this project apart is its focus on correctness, stability, and transparency rather than black-box results. I work with signed distance functions (SDFs) and differentiable rendering to jointly optimize object geometry and camera poses, making the system suitable for textureless or calibration-free scenarios. Due to computational constraints, training is scoped to a limited number of objects, which allows for controlled experimentation and reliable evaluation.
If you are looking for a solid baseline, research prototype, or exploratory implementation of neural implicit 3D reconstruction, this project provides a dependable and well-documented starting point.
What sets this project apart is its focus on correctness, stability, and transparency rather than black-box results. I work with signed distance functions (SDFs) and differentiable rendering to jointly optimize object geometry and camera poses, making the system suitable for textureless or calibration-free scenarios. Due to computational constraints, training is scoped to a limited number of objects, which allows for controlled experimentation and reliable evaluation.
If you are looking for a solid baseline, research prototype, or exploratory implementation of neural implicit 3D reconstruction, this project provides a dependable and well-documented starting point.
Machine Learning Tools
NumPy, OpenCV, Python, PyTorchWhat's included
| Service Tiers |
Starter
$80
|
Standard
$160
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 1 | 2 |
Number of Graphs/Charts | 0 | 1 | 0 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Frequently asked questions
About Nissi
Computer Vision & Machine Learning Engineer |Pose Estimation, 3D Recon
Kottayam, India - 4:45 am local time
-Building CV pipelines: pose estimation, object detection, segmentation, tracking.
-Training and troubleshooting ML models (PyTorch/TensorFlow).
-3D reconstruction work on SDFs, neural implicit models, multi-view geometry.
-Image correspondence tasks using SIFT/SuperGlue.
-Creating simple, clean UIs for models (Gradio or React).
-Writing clear documentation and making projects reproducible.
I like keeping communication simple, sharing progress often, and making sure the final solution is something you can actually use and understand.
If you have an idea or dataset you want to explore, I’d be happy to help.
- Nissi :)
Steps for completing your project
After purchasing the project, send requirements so Nissi can start the project.
Delivery time starts when Nissi receives requirements from you.
Nissi works on your project following the steps below.
Revisions may occur after the delivery date.
Review data and requirements
Check input images, validate feasibility, and finalize reconstruction scope.
Neural implicit model setup
Configure the PMNI-based pipeline and prepare training inputs.