DATR: Diffusion-based 3D Apple Tree Reconstruction Framework with Sparse-View

• URL: http://arxiv.org/abs/2508.19508v1
• Date: Wed, 27 Aug 2025 01:45:54 GMT
• Title: DATR: Diffusion-based 3D Apple Tree Reconstruction Framework with Sparse-View
• Abstract summary: This study developed a two-stage framework for the reconstruction of apple trees from sparse views.<n>The framework was evaluated on both field and synthetic datasets.
• Score: 8.493134772085233
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Digital twin applications offered transformative potential by enabling real-time monitoring and robotic simulation through accurate virtual replicas of physical assets. The key to these systems is 3D reconstruction with high geometrical fidelity. However, existing methods struggled under field conditions, especially with sparse and occluded views. This study developed a two-stage framework (DATR) for the reconstruction of apple trees from sparse views. The first stage leverages onboard sensors and foundation models to semi-automatically generate tree masks from complex field images. Tree masks are used to filter out background information in multi-modal data for the single-image-to-3D reconstruction at the second stage. This stage consists of a diffusion model and a large reconstruction model for respective multi view and implicit neural field generation. The training of the diffusion model and LRM was achieved by using realistic synthetic apple trees generated by a Real2Sim data generator. The framework was evaluated on both field and synthetic datasets. The field dataset includes six apple trees with field-measured ground truth, while the synthetic dataset featured structurally diverse trees. Evaluation results showed that our DATR framework outperformed existing 3D reconstruction methods across both datasets and achieved domain-trait estimation comparable to industrial-grade stationary laser scanners while improving the throughput by $\sim$360 times, demonstrating strong potential for scalable agricultural digital twin systems.

Related papers

• Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation [68.73567966621631]
  We revisit Marigold, a set of techniques for repurposing modern image generation and editing models, into state-of-the-art monocular depth estimators.<n>Our recipes target single-step inference from pretrained multi-step flow-matching models, with quantization where needed, preserving model capacity while remaining cheap to run.<n>The results are crisper, cleaner depth maps that generalize well out-of-distribution, with 16-26% improvement in AbsRel over the previous best on KITTI and ETH3D.
  arXiv  Detail & Related papers  (2026-09-08T00:52:01Z)
• Occlusion-Robust Multi-Object Decoupling for Physics-Based Robotic Interaction [33.71202994777273]
  We propose a mask-free method for multi-object 3D reconstruction from sparse and occluded real-world views.<n>Our method produces simulation-ready 3D objects without requiring manual masks.
  arXiv  Detail & Related papers  (2026-06-28T09:50:46Z)
• Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation [63.01022057888141]
  Asset Harvester is an image-to-3D model and end-to-end pipeline that converts sparse, in-thewild object observations from real driving logs into complete, simulation-ready assets.<n>SparseViewDiT is explicitly designed to address limited-angle views and other real-world data challenges.
  arXiv  Detail & Related papers  (2026-04-20T16:20:57Z)
• LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows [10.300202521638274]
  We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows impacts feed-forward 3D reconstruction.<n>We show that expanding the context window -- by substantially increasing the number of active object and image tokens -- remarkably narrows this gap and enables high-fidelity 3D object reconstruction and inverse rendering.
  arXiv  Detail & Related papers  (2026-04-06T21:21:12Z)
• Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learning [6.254251081017878]
  This paper introduces a benchmark to evaluate the performance of vision language models (VLMs) in generating plant simulation for digital twins.<n>We propose a novel approach that leverages state-of-the-art open-source VLMs to directly generate simulation parameters in format from drone-based remote sensing images.
  arXiv  Detail & Related papers  (2026-03-09T20:58:43Z)
• LARM: A Large Articulated-Object Reconstruction Model [29.66486888001511]
  LARM is a unified feedforward framework that reconstructs 3D articulated objects from sparse-view images.<n>LARM generates auxiliary outputs such as depth maps and part masks to facilitate explicit 3D mesh extraction and joint estimation.<n>Our pipeline eliminates the need for dense supervision and supports high-fidelity reconstruction across diverse object categories.
  arXiv  Detail & Related papers  (2025-11-14T18:55:27Z)
• Vector Representations of Vessel Trees [12.391128284848135]
  We introduce a novel framework for learning vector representations of tree-structured geometric data focusing on 3D vascular networks.<n>Our framework, named VeTTA, offers precise, flexible, and topologically consistent modeling of anatomical tree structures in medical imaging.
  arXiv  Detail & Related papers  (2025-06-11T20:34:08Z)
• AgriFM: A Multi-source Temporal Remote Sensing Foundation Model for Crop Mapping [11.187551725609099]
  Transformer-based remote sensing foundation models (RSFMs) offer potential for crop mapping due to their ability for unified processing.<n>We present AgriFM, a multi-temporal remote sensing foundation model specifically designed for agricultural crop mapping.
  arXiv  Detail & Related papers  (2025-05-27T15:50:14Z)
• UVRM: A Scalable 3D Reconstruction Model from Unposed Videos [68.34221167200259]
  Training 3D reconstruction models with 2D visual data traditionally requires prior knowledge of camera poses for the training samples.<n>We introduce UVRM, a novel 3D reconstruction model capable of being trained and evaluated on monocular videos without requiring any information about the pose.
  arXiv  Detail & Related papers  (2025-01-16T08:00:17Z)
• DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models [67.50989119438508]
  We introduce DSplats, a novel method that directly denoises multiview images using Gaussian-based Reconstructors to produce realistic 3D assets.<n>Our experiments demonstrate that DSplats not only produces high-quality, spatially consistent outputs, but also sets a new standard in single-image to 3D reconstruction.
  arXiv  Detail & Related papers  (2024-12-11T07:32:17Z)
• DiHuR: Diffusion-Guided Generalizable Human Reconstruction [51.31232435994026]
  We introduce DiHuR, a Diffusion-guided model for generalizable Human 3D Reconstruction and view synthesis from sparse, minimally overlapping images.<n>Our method integrates two key priors in a coherent manner: the prior from generalizable feed-forward models and the 2D diffusion prior, and it requires only multi-view image training, without 3D supervision.
  arXiv  Detail & Related papers  (2024-11-16T03:52:23Z)
• DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features [65.8738034806085]
  DistillNeRF is a self-supervised learning framework for understanding 3D environments in autonomous driving scenes. Our method is a generalizable feedforward model that predicts a rich neural scene representation from sparse, single-frame multi-view camera inputs.
  arXiv  Detail & Related papers  (2024-06-17T21:15:13Z)
• GeoGen: Geometry-Aware Generative Modeling via Signed Distance Functions [22.077366472693395]
  We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections. By employing volumetric rendering using neural radiance fields, they inherit a key limitation: the generated geometry is noisy and unconstrained. We propose GeoGen, a new SDF-based 3D generative model trained in an end-to-end manner.
  arXiv  Detail & Related papers  (2024-06-06T17:00:10Z)
• CryoFormer: Continuous Heterogeneous Cryo-EM Reconstruction using Transformer-based Neural Representations [49.49939711956354]
  Cryo-electron microscopy (cryo-EM) allows for the high-resolution reconstruction of 3D structures of proteins and other biomolecules. It is still challenging to reconstruct the continuous motions of 3D structures from noisy and randomly oriented 2D cryo-EM images. We propose CryoFormer, a new approach for continuous heterogeneous cryo-EM reconstruction.
  arXiv  Detail & Related papers  (2023-03-28T18:59:17Z)
• Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversion [54.151979979158085]
  We introduce a principled end-to-end reconstruction framework for natural images, where accurate ground-truth poses are not available. We leverage an unconditional 3D-aware generator, to which we apply a hybrid inversion scheme where a model produces a first guess of the solution. Our framework can de-render an image in as few as 10 steps, enabling its use in practical scenarios.
  arXiv  Detail & Related papers  (2022-11-21T17:42:42Z)
• Ground material classification and for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach [1.3359609092684614]
  In recent years, photogrammetry has been widely used in many areas to create 3D virtual data representing the physical environment. These cutting-edge technologies have caught the US Army and Navy's attention for the purpose of rapid 3D battlefield reconstruction, virtual training, and simulations.
  arXiv  Detail & Related papers  (2021-09-24T22:29:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.