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"content": "The challenge features **14 curated food combo scenes**, each containing multiple food items accompanied by utensils and plates. These items are meticulously captured using a **high-precision 3D scanner**.\n\nUtensils and plates are intentionally included to serve as **implicit physical references** for scale, helping ensure that the reconstructed 3D models are not only visually accurate but also **physically accurate in size**—a key evaluation metric.\n\nTo build this dataset, we follow the **MetaFood3D** data collection pipeline [[1]](#metafood3d). Each food item is individually scanned and annotated.\n\n\n\nThe difficulty level of the challenge varies with the number of food items, and occlusions caused by camera angles. Detailed information on these food items is provided below (note that the same broccoli appears twice):\n\n| Combo Index | Combo Name | Food Index | Food Name | With Container? | Private or Public |\n|-------------|-----------------------------------|------------|-------------------|------------------|--------------------|\n| 1 | bagel+cream_cheese | 1 | bagel | | Public |\n| | | 2 | cream_cheese | | Public |\n| 2 | breaded_fish+lemon+broccoli | 3 | breaded_fish | | Public |\n| | | 4 | lemon | | Public |\n| | | 5 | broccoli | | Public |\n| 3 | burger+hot_dog | 6 | burger | | Public |\n| | | 7 | hotdog | | Public |\n| 4 | cheesecake+strawberry+raspberry | 8 | cheesecake | | Public |\n| | | 9 | strawberry | | Public |\n| | | 10 | raspberry | | Public |\n| 5 | energy_bar+cheddar_cheese+banana | 11 | energy_bar | | Private |\n| | | 12 | cheddar_cheese | | Private |\n| | | 13 | banana | | Private |\n| 6 | grilled_salmon+broccoli | 14 | grilled_salmon | | Private |\n| | | 5 | broccoli | | Public |\n| 7 | pasta+garlic_bread | 15 | pasta | With Bowl | Private |\n| | | 16 | garlic_bread | | Private |\n| 8 | pb&j+carrot_stick+apple+celery | 17 | pb&j | | Private |\n| | | 18 | carrot_stick | | Private |\n| | | 19 | apple | | Private |\n| | | 20 | celery | | Private |\n| 9 | pizza+chicken_wing | 21 | pizza | | Private |\n| | | 22 | chicken_wing | | Private |\n| 10 | quesadilla+guacamole+salsa | 23 | quesadilla | | Private |\n| | | 24 | guacamole | With Cup | Private |\n| | | 25 | salsa | With Cup | Private |\n| 11 | roast_chicken_leg+biscuit | 26 | roast_chicken_leg | | Private |\n| | | 27 | biscuit | | Private |\n| 12 | sandwich+cookie | 28 | sandwich | | Private |\n| | | 29 | cookie | | Private |\n| 13 | steak+mashed_potatoes | 30 | steak | | Private |\n| | | 31 | mashed_potatoes | With Bowl | Private |\n| 14 | toast+sausage+fried_egg | 32 | toast | | Private |\n| | | 33 | sausage | | Private |\n| | | 34 | fried_egg | | Private |\n\n\n---\n\n\n### 📚 Reference\n\n[1] Chen, Yuhao, et al. \"*MetaFood3D: Large 3D Food Object Dataset with Nutrition Values.*\" *arXiv preprint arXiv:2409.01966* (2024).",
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"content": "**This year's challenge will emphasize multi-food scenarios without explicit physical references. **\nThese changes are motivated to align the challenge more closely with real-world applications. By focusing on multi-food scenarios, we aim to address the complexity of actual eating occasions where multiple items with diverse shapes, textures, and reflective properties coexist on a plate. In addition, the removal of explicit physical references encourages the development of algorithms that can infer scale and orientation from implicit cues (such as plates, utensils, or common food items of known size) found in natural eating settings. This realistic setup not only enhances the challenge's relevance to practical applications in nutrition tracking and dietary assessment but promotes the creation of more intelligent and adaptive computer vision solutions. \n\nTo start, we recommend you use image to 3D generation model to create a mesh. Then use metric depth estimation model to calibrate the size. See rules for the recommended external resources.\n\n\n",
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"name": "Evaluation",
"content": "The evaluation will consist of a two-phase process focused on assessing the precision of the reconstructed 3D models in terms of both **shape (3D structure)** and **portion size (volume)**. Results will be compared against reference models of the same food items captured using a 3D scanner.\n\n### Phase I: Volume Accuracy (measured in mL)\n\nIn Phase I, we will use the **Mean Absolute Percentage Error (MAPE)** metric to evaluate the accuracy of predicted portion sizes. Only **volume accuracy** will be considered during this stage.\n\n### Phase II: Shape Accuracy (Top Teams) (measured in mm^3)\n\nThe top-ranking teams (3-5, depends on the total number of teams) from Phase I will proceed to Phase II, where they will submit **complete 3D mesh files** for each food item. We will use the **L1 Chamfer Distance** metric to evaluate the **shape accuracy** of the models. Our evaluation protocol follows the Chamfer Distance metric used in **DTU** benchmark [[1]](#dtu).\n\nThe **final winner** will be determined by combining scores from both phases for all private food objects. Each team will receive a ranking (1–5) in each phase, and the final score will be a weighted sum:\n\n- **Phase I (Volume Accuracy):** 55% weight \n- **Phase II (Shape Accuracy):** 45% weight\n\n---\n\n## 📦 Phase II Evaluation (Top Teams)\n\n### 1. Upload Models \nSubmit all private **reconstructed 3D models** as `.obj` files, each named according to the corresponding **object ID**. Upload the files to a downloadable location (e.g., Google Drive, Dropbox).\n\n### 2. Send the Download Link \nEmail the **download link** and your **team ID** to us by **May 13rd, 11:59 PM AOE**.\n\n- **Email:** `metafood.cvpr@gmail.com` \n- For support, post in the **Kaggle discussion forum**.\n\n### 3. Model Verification \nWe will verify your submitted models against your final Phase I predictions and visually inspect them for compliance. \n_Models composed of primitive shapes (e.g., spheres or cubes) will be disqualified._\n\n### 4. Ground Truth Models & Evaluation Script \nWe will release the **ground truth 3D models** and **Chamfer Distance evaluation script** on Kaggle by **May 13rd, 11:59 PM AOE**.\n\n### 5. Align Models \nYou must align your models to the ground truth by preparing a **transformation matrix** for each object. \nWe will compute your Chamfer Distance score as follows:\n\n- Randomly sample 16384 points from each transformed point cloud, **10 times** \n- Compute the **L1-Chamfer Distance** for each sample \n- Average the 10 scores to obtain your **final Chamfer Distance score**\n\n### 6. Submit Transformation Matrices \nEmail us your transformation matrices by **May 16th, 11:59 PM AOE**.\n\n---\n\n### 🏆 Winner Announcement \nThe winning team will be announced on **May 18th**.\n\n\n## Submission File\nFor each id in the test set, you must reconstruct a 3D model from the provided images and report the volume of the model. The file should contain a header and have the following format:\n\n id,predicted\n 2,0\n 5,0\n 6,0\n etc.\n\n\n### 📚 Reference\n\n[1] Aanæs, Henrik, et al. \"*Large-scale data for multiple-view stereopsis.*\" *International Journal of Computer Vision*, 120 (2016): 153–168.\n",
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"name": "rules",
"content": "## 📝 Challenge Rules\n\nParticipants are required to submit all **private `.obj` mesh files** corresponding to the selected private food items. These submissions will be evaluated based on **shape and size accuracy** of the reconstruction.\n\nPlease carefully review the following rules:\n\n### ✅ Submission Guidelines\n\n- Submissions **must follow the provided instructions**. Submissions will be disqualified if they contain inappropriate content or do not meet the official rules.\n- Each registered participant or team may **submit once per day**.\n- The contest is **open to all**. However, individuals involved in organizing or supporting this competition are **not eligible to participate**.\n- Participants may use **publicly available data and models**, but **must provide proper attribution** by including references in their submitted code.\n- During the **final evaluation phase**, the organizers will thoroughly inspect the submitted code and 3D models of top-performing teams. \n Disqualification will occur in the following cases:\n - Use of plagiarized or similar code to other teams.\n - Submissions lacking substantial original work.\n - Submissions with 3D models that do **not represent the correct shape of the object class**. \n For example: \n ❌ Submitting a **sphere** for the class `strawberry` \n ✅ Submitting a **hemisphere with a cone** for the class `strawberry`\n- Mesh does not have to be watertight.\n- Contestants are fully responsible for ensuring their submissions, including files, data, and code, are **legal and comply with all regulations**. \n The organizers **assume no legal liability** for issues that arise from participant submissions.\n- If participants publish papers related to their results in this competition, **they must acknowledge the competition**.\n\n### 🏆 Winner and Prize\n\n- The **top-3 teams** will be selected as winners.\n- Winners will be **invited for an oral presentation** at the MetaFood workshop and will be asked to write a **technical report** about the challenge.\n- Visit the [MetaFood 2025 Workshop Website](https://sites.google.com/view/cvpr-metafood-2025) for prize details.\n\n---\n\n## 📦 Use of External Resources\n\nParticipants are allowed to use **external resources**, provided **sources are clearly cited** in the submission. Here are some external resources you can start with.\n\n### 3D Generation Models\n- TRELLIS [[1]](#references)\n- Hunyuan3D 2.0 [[2]](#references)\n\n### 3D Object Datasets\n- OmniObject3D [[3]](#references)\n- NutritionVerse-3D [[4]](#references)\n- MetaFood3D [[5]](#references)\n- Objaverse [[6]](#references)\n\n### Depth Estimation Models\n- DepthPro [[7]](#references)\n- Depth Anything V2 [[8]](#references)\n\n---\n\n## 📚 References\n\n\n\n[1] Xiang, Jianfeng, et al. \"Structured 3D Latents for Scalable and Versatile 3D Generation.\" *arXiv preprint arXiv:2412.01506* (2024). \n[2] Zhao, Zibo, et al. \"Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation.\" *arXiv preprint arXiv:2501.12202* (2025). \n[3] Wu, Tong, et al. \"OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation.\" *CVPR 2023*. \n[4] Tai, Chi-en Amy, et al. \"NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation.\" *arXiv preprint arXiv:2304.05619*, 2023. \n[5] Chen, Yuhao, et al. \"MetaFood3D: Large 3D Food Object Dataset with Nutrition Values.\" *arXiv preprint arXiv:2409.01966* (2024). \n[6] Deitke, Matt, et al. \"Objaverse-XL: A Universe of 10M+ 3D Objects.\" *NeurIPS 2023*, pp. 35799–35813. \n[7] Bochkovskii, Aleksei, et al. \"DepthPro: Sharp Monocular Metric Depth in Less Than a Second.\" *arXiv preprint arXiv:2410.02073* (2024). \n[8] Yang, Lihe, et al. \"Depth Anything V2.\" *NeurIPS 2024*, pp. 21875–21911.\n\n---\n",
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"name": "abstract",
"content": "3D reconstruction from monocular images is a rapidly evolving research area in computer vision with significant applications in food image analysis. The ability to reconstruct 3D food models from single 2D eating occasion images in real-world physical units allows users to share food experiences in three dimensions and provides crucial information about food portions, facilitating the tracking of individual nutrition intake. However, 3D reconstruction presents unique challenges that make it particularly valuable to evaluate the robustness and capability of existing computer vision algorithms. Specifically, food items in in-the-wild images exhibit a wide variety of colors, textures, shapes, and sizes. Their reflective properties can vary drastically, from the glossy surface such as a boiled egg to the matte exterior such as yeast bread. Furthermore, when multiple food items are placed together, occlusions, overlapping, and shadows introduce complex scenarios that a robust algorithm needs to extract useful information. Additionally, food is rarely uniform where the same dish can be presented in multiple ways, making approaches using predefined 3D models or shapes as references insufficient. Therefore, evaluating CV/AI algorithms on 3D food reconstruction pushes the boundaries of current technologies and encourages them to handle high intra-class variability and inter-class data with complex spatial arrangements. \n\nCVPR2025 MetaFood workshop site:\nhttps://sites.google.com/view/cvpr-metafood-2025/\n\n**The deadline of this competition has been extended to May 11 2025**",
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"content": "The following Kaggle Competition Foundational Rules (“ Foundational Rules ”) apply to every competition regardless of whether the Sponsor creates competition-specific rules. Any competition-specific rules provided by the Sponsor are in addition to these rules, and in the case of any conflict or inconsistency, these Foundational Rules control and nullify contrary competition-specific rules.\n###GENERAL COMPETITION RULES - BINDING AGREEMENT\n####1. ELIGIBILITY\na. To be eligible to enter the Competition, you must be:\n1. a registered account holder at Kaggle.com; \n2. the older of 18 years old or the age of majority in your jurisdiction of residence (unless otherwise agreed to by Competition Sponsor and appropriate parental/guardian consents have been obtained by Competition Sponsor); \n3. not a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, or North Korea; and\n4. not a person or representative of an entity under U.S. export controls or sanctions (see: [https://www.treasury.gov/resourcecenter/sanctions/Programs/Pages/Programs.aspx][1]).\n\nb. Competitions are open to residents of the United States and worldwide, except that if you are a resident of Crimea, so-called Donetsk People's Republic (DNR) or Luhansk People's Republic (LNR), Cuba, Iran, Syria, North Korea, or are subject to U.S. export controls or sanctions, you may not enter the Competition. 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Unless otherwise specifically permitted under the Competition Website or Competition Specific Rules above, during the Competition Period, you are not allowed to privately share source or executable code developed in connection with or based upon the Competition Data or other source or executable code relevant to the Competition (“Competition Code”). This prohibition includes sharing Competition Code between separate Teams, unless a Team merger occurs. Any such sharing of Competition Code is a breach of these Competition Rules and may result in disqualification.\nb. Public Code Sharing. You are permitted to publicly share Competition Code, provided that such public sharing does not violate the intellectual property rights of any third party. If you do choose to share Competition Code or other such code, you are required to share it on Kaggle.com on the discussion forum or notebooks associated specifically with the Competition for the benefit of all competitors. 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Competition Entities are not responsible for any malfunction of the Competition Website or any late, lost, damaged, misdirected, incomplete, illegible, undeliverable, or destroyed Submissions or entry materials due to system errors, failed, incomplete or garbled computer or other telecommunication transmission malfunctions, hardware or software failures of any kind, lost or unavailable network connections, typographical or system/human errors and failures, technical malfunction(s) of any telephone network or lines, cable connections, satellite transmissions, servers or providers, or computer equipment, traffic congestion on the Internet or at the Competition Website, or any combination thereof, which may limit a Participant’s ability to participate.\n\n####16. RIGHT TO CANCEL, MODIFY OR DISQUALIFY\na. If for any reason the Competition is not capable of running as planned, including infection by computer virus, bugs, tampering, unauthorized intervention, fraud, technical failures, or any other causes which corrupt or affect the administration, security, fairness, integrity, or proper conduct of the Competition, Competition Sponsor reserves the right to cancel, terminate, modify or suspend the Competition. Competition Sponsor further reserves the right to disqualify any Participant who tampers with the submission process or any other part of the Competition or Competition Website. Any attempt by a Participant to deliberately damage any website, including the Competition Website, or undermine the legitimate operation of the Competition is a violation of criminal and civil laws. Should such an attempt be made, Competition Sponsor and Kaggle each reserves the right to seek damages from any such Participant to the fullest extent of the applicable law.\n\n####17. NOT AN OFFER OR CONTRACT OF EMPLOYMENT\na. Under no circumstances will the entry of a Submission, the awarding of a Prize, or anything in these Rules be construed as an offer or contract of employment with Competition Sponsor or any of the Competition Entities. You acknowledge that you have submitted your Submission voluntarily and not in confidence or in trust. You acknowledge that no confidential, fiduciary, agency, employment or other similar relationship is created between you and Competition Sponsor or any of the Competition Entities by your acceptance of these Rules or your entry of your Submission.\n\n####18. DEFINITIONS\na. \"Competition Data\" are the data or datasets available from the Competition Website for the purpose of use in the Competition, including any prototype or executable code provided on the Competition Website. The Competition Data will contain private and public test sets. Which data belongs to which set will not be made available to Participants. \nb. An “Entry” is when a Participant has joined, signed up, or accepted the rules of a competition. Entry is required to make a Submission to a competition.\nc. A “Final Submission” is the Submission selected by the user, or automatically selected by Kaggle in the event not selected by the user, that is/are used for final placement on the competition leaderboard.\nd. A “Participant” or “Participant User” is an individual who participates in a competition by entering the competition and making a Submission.\ne. The “Private Leaderboard” is a ranked display of Participants’ Submission scores against the private test set. The Private Leaderboard determines the final standing in the competition.\nf. The “Public Leaderboard” is a ranked display of Participants’ Submission scores against a representative sample of the test data. This leaderboard is visible throughout the competition.\ng. A “Sponsor” is responsible for hosting the competition, which includes but is not limited to providing the data for the competition, determining winners, and enforcing competition rules.\nh. A “Submission” is anything provided by the Participant to the Sponsor to be evaluated for competition purposes and determine leaderboard position. A Submission may be made as a model, notebook, prediction file, or other format as determined by the Sponsor.\ni. A “Team” is one or more Participants participating together in a Kaggle competition, by officially merging together as a Team within the competition platform.\n\n [1]: https://www.treasury.gov/resource-center/sanctions/Programs/Pages/Programs.aspx\n [2]: http://www.kaggle.com/terms\n [3]: http://www.opensource.org\n [4]: http://www.opensource.org\n [5]: https://www.kaggle.com/WinningModelDocumentationGuidelines\n [6]: http://www.kaggle.com/privacy\n [7]: http://www.kaggle.com/contact\n",
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