LiDAR Semantic Segmentation Annotation

@HiraMahmood4072

Hello, I have over 7 years of experience in Data Processing, Machine Learning, Data Science, and Data Management. I have carefully read the requirements for the LiDAR Semantic Segmentation Annotation project. To complete this project, I will meticulously annotate each point in the raw LiDAR point-cloud maps according to the predefined schema provided. I will ensure that every point belonging to a vehicle, building, pedestrian, and other classes is accurately labeled for deep-learning purposes. I will utilize professional tools such as CloudCompare or Labelbox to maintain the original format and coordinate system of the finished files. The deliverables will include complete, per-point annotations, a log of any edge cases or assumptions, and a quality-control summary. The acceptance criteria will be met by ensuring that 100% of points carry the required classes and that the output loads flawlessly in standard .las viewers. I would like to discuss this project further in chat to provide a detailed plan for its successful completion. You can visit my Profile: https://www.freelancer.com/u/HiraMahmood4072 Thank you.

@hotanloc

Dear, I am excited about the opportunity to assist you with the fine-grained labeling of your LiDAR point-cloud maps. With extensive experience in 3D data annotation and deep-learning pipelines, I can ensure accurate per-point labeling for vehicles, buildings, pedestrians, and other classes as per your predefined schema. I am comfortable using professional tools such as CloudCompare and Labelbox and will maintain the original format and coordinate system throughout the process. I will meticulously review the existing classifications, correcting labels and extending them to more specific categories as needed. Deliverables will include complete annotations for every supplied map, along with a log documenting any edge cases or assumptions made during the labeling process. Additionally, I will provide a quality-control summary to verify class accuracy and ensure that no points remain unlabeled. I am committed to achieving 100% compliance with your acceptance criteria and will ensure that the output seamlessly integrates with standard .las viewers, retaining all essential metadata. I am ready to commence work upon your approval of the sample scene, and I look forward to delivering high-quality results for your dataset. Thank you for considering my bid. Best regards, Loc

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Data Processing, Machine Learning (ML), Data Science, Segment, Computer Vision, Deep Learning, Data Management, AI Model Development, AI Research and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@Feriver

Hello, I can create high-quality per-point annotations for your LiDAR point-cloud maps, ensuring every point is labeled according to your provided schema. I have experience with semantic segmentation and deep-learning datasets, using tools like CloudCompare, Labelbox, and custom pipelines. I will correct existing automated labels, refine them for fine-grained classes (vehicles, buildings, pedestrians, etc.), and ensure all points are accurately annotated without losing coordinate system integrity. Deliverables include fully labeled point clouds in the original format, a log of edge cases or assumptions, and a QC summary confirming class accuracy and completeness. Each file will load seamlessly in standard .las viewers with all metadata preserved. Thanks, Asif

@SkillCrafts

Your requirement for LiDAR semantic segmentation aligns perfectly with my background in processing complex 3D datasets for perception systems. I’ve recently completed projects involving large-scale urban point clouds where I delivered sub-centimeter accuracy for object boundaries, ensuring the training data was robust for ML models. I recognize that the integrity of your set depends on the meticulous per-point classification of every return, especially in dense environments where noise and occlusion often compromise automated labeling attempts. I process raw .pcd or .las files in high-performance environments like CVAT or Supervisely to maintain full resolution without downsampling. My workflow utilizes a hybrid approach: first applying geometric surface analysis to isolate ground and static infrastructure, followed by rigorous manual segmentation of dynamic actors and fine-grained features. To ensure zero-defect delivery, I perform topological checks to identify outliers and ensure class distribution matches your requirements, providing final outputs in SemanticKITTI or custom NumPy formats. Could you specify the target class ontology and if there are specific edge cases, such as thin vertical structures, that require extra attention? Additionally, I'd like to know the average frame count per map to accurately estimate the processing timeline. I’m available for a quick message exchange or call to align on your quality benchmarks, and I am happy to process a small data sample first to demonstrate my precision before we scale the project.

@ssldevmaster

Hi, I’ve worked with LiDAR point clouds and can deliver precise per-point semantic labeling aligned with your schema. My approach: refine auto-labeled data → ensure class consistency → validate completeness with QC checks. Quick question: what format are your files in (.las/.laz/.pcd) and any preferred annotation tool? I’ll ensure 100% labeled output, clean metadata, and a QC summary ready for your ML pipeline.

@DngPhgNam

⭐⭐⭐⭐⭐ ✅Hi there, hope you are doing well! I have previously worked on projects involving detailed point-cloud segmentation where fine-grained labeling was critical to train deep-learning models effectively and efficiently. The most important aspect to successfully complete this project is ensuring 100% accurate per-point labeling aligned precisely with your predefined schema and maintaining the original coordinate system. Approach: ⭕ I will start by reviewing your provided sample scene and class schema thoroughly. ⭕ Use professional tools like CloudCompare or Labelbox for detailed per-point corrections and annotations. ⭕ Maintain all metadata and coordinate systems intact to ensure seamless loading in standard .las viewers. ⭕ Create a quality-control summary to confirm class accuracy and completeness of labeling. ⭕ Provide a log documenting any edge cases or assumptions made during labeling. ❓ Could you please clarify the expected delivery format for the labeled data? Will it be strictly .las or are other formats acceptable? I am confident in delivering precise and quality annotations that meet your requirements and are ready to start promptly. Thank you for considering my proposal. Best regards, Nam

@johnedwardl

Hi, I will transform your raw LiDAR point-cloud maps into precisely segmented training data, ensuring every point is accurately labeled according to your schema. With extensive experience in per-point annotation and tools like CloudCompare and Labelbox, I’ll deliver the high-quality dataset you need for your deep-learning pipeline. My approach will focus on correcting existing labels and extending them to meet your detailed class definitions, guaranteeing that every point is properly categorized. I’ll maintain the original format and coordinate system throughout the process, and I’ll provide a quality-control summary to confirm class accuracy and that all points are labeled. To ensure a smooth workflow, I’d appreciate any specific guidelines regarding edge cases or assumptions you want me to consider. Once the sample scene is approved, I’ll efficiently proceed with the remaining datasets. Let’s get started on this project to enhance your data quality. Thank you.

@engruhulajom

Hello, Hope you are doing fine. I have extensive experience with LiDAR point-cloud annotation using CloudCompare and custom scripts, including fine-grained per-point labeling for semantic segmentation. I understand your need to correct and extend an automated model’s output according to your schema while preserving original format and coordinates. I will: - Use your reference scene to verify labeling accuracy. - Process each map ensuring every point is tagged with one of your defined classes. - Log edge cases and assumptions. - Deliver the annotated files in .las format with metadata intact. - Provide a quality summary confirming class coverage. Ready to start once I receive your schema, reference scene, and dataset. Best regards, Md Ruhul Ajom

@huonghy89

Hi, I hope you are doing well. Very happy to bid your project because my skills are fitted in your project. I have extensive experience in LiDAR point-cloud processing and fine-grained semantic segmentation, including per-point annotation for autonomous driving and urban mapping datasets. I will carefully review and correct your pre-labeled point clouds to ensure precise, per-point classification according to your schema while preserving original formats and metadata. I will also perform thorough quality control to guarantee 100% labeling completeness and provide a clear report on edge cases and accuracy. If you send the message, we can discuss the project more. Thanks.

@mujtabarehman122

Per-point annotation breaks down at object boundaries. Occluded vehicles, pedestrians clipped by scan shadow, ground returns bleeding into low-clearance undersides. That is exactly where automated pre-labels fail and where careful correction determines whether the dataset is actually trainable. I have done per-point annotation on outdoor scene point clouds using CloudCompare and SemanticKITTI format datasets, correcting automated labels and extending to fine-grained class schemas. Output maintained in original coordinate system with all metadata intact throughout. Since your automated model has handled bulk classification, the work is correction and schema extension. I would validate my class interpretation against your reference scene first, then move through the dataset systematically with edge case logging and per-class accuracy summary per batch. Ready to start immediately. Can you share the reference scene and class schema so I can give you an accurate per-scene time estimate?