Receding-Horizon Next-Best-View Planner for Autonomous Leaf Surface Reconstruction

• URL: http://arxiv.org/abs/2607.28995v1
• Date: Fri, 31 Jul 2026 03:45:51 GMT
• Title: Receding-Horizon Next-Best-View Planner for Autonomous Leaf Surface Reconstruction
• Abstract summary: We study next-best-view (NBV) planning for leaf surface reconstruction under travel constraints.<n>We develop a novel Centroid-based Information Gain function that measures the spatial distribution of observed points.<n>The proposed receding-horizon approach consistently reduces surface reconstruction error and improves geometric fidelity across multiple growth stages.
• Score: 6.699826218138504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate plant leaf modeling is fundamental to downstream tasks such as plant growth monitoring, and phenotyping for yield estimation. Autonomous robotic reconstruction for large-scale field deployment must address limitations on robot planning budget and computation resources while optimizing viewpoint utility for leaf surface reconstruction. Existing approaches either focus on rigid objects, point-cloud coverage or plant reconstruction without fully addressing the system limitations or exploiting task-driven point cloud utility. In this work, we study next-best-view (NBV) planning for leaf surface reconstruction under travel constraints. We develop a novel Centroid-based Information Gain (CIG) function that measures the spatial distribution of observed points relative to the centroid of the existing point cloud to compute viewpoint utility. We also develop a receding-horizon variant that reasons over future viewpoints. To benchmark our work, we use the LAST-STRAW [1] public dataset that includes point clouds of strawberry plants over different growth stages and compare our method with attention-driven NBV [2] that uses a visibility-based information gain approach. The proposed receding-horizon approach consistently reduces surface reconstruction error and improves geometric fidelity across multiple growth stages, especially under increased inter-leaf occlusion. Results demonstrate that our approach is able to visit viewpoints that reduce surface reconstruction error and improves reconstruc-tion accuracy as compared to the baseline by upto 10%.

Related papers

• Z-FLoc: Zero-Shot Floorplan Localization via Geometric Primitives [72.63181031215858]
  We propose a zero-shot floorplan localization method that generalizes to novel environments without any retraining.<n>Our key insight is that dominant geometric primitives are ubiquitous in human-made environments.<n> Experiments on both simulated and real-world datasets show that our approach outperforms state-of-the-art learning-based methods on unseen environments.
  arXiv  Detail & Related papers  (2026-06-03T12:14:24Z)
• StepVAR: Structure-Texture Guided Pruning for Visual Autoregressive Models [98.72926158261937]
  We propose a training-free token pruning framework for Visual AutoRegressive models.<n>We employ a lightweight high-pass filter to capture local texture details, while leveraging Principal Component Analysis (PCA) to preserve global structural information.<n>To maintain valid next-scale prediction under sparse tokens, we introduce a nearest neighbor feature propagation strategy.
  arXiv  Detail & Related papers  (2026-03-02T11:35:05Z)
• Reconstructing Building Height from Spaceborne TomoSAR Point Clouds Using a Dual-Topology Network [17.230182354159258]
  We introduce a learning-based framework for converting raw TomoSAR points into high-resolution building height maps.<n>Our dual-topology network alternates between a point branch that models irregular scatterer features and a grid branch that enforces spatial consistency.<n>This is the first proof of concept for large-scale urban height mapping directly from TomoSAR point clouds.
  arXiv  Detail & Related papers  (2026-01-02T11:34:35Z)
• GSPlane: Concise and Accurate Planar Reconstruction via Structured Representation [30.083162532688096]
  We propose GSPlane, which recovers accurate geometry and produces clean and well-structured mesh connectivity for plane regions.<n>We also explore applications of the structured planar representation, which enable decoupling and flexible manipulation of objects on supportive planes.
  arXiv  Detail & Related papers  (2025-10-20T01:59:21Z)
• PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport [56.56430888985025]
  We propose a generative adversarial network for point cloud quality enhancement (PCE-GAN)<n>The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit.<n>The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality reconstruction.
  arXiv  Detail & Related papers  (2025-02-26T07:34:33Z)
• PVP-Recon: Progressive View Planning via Warping Consistency for Sparse-View Surface Reconstruction [49.7580491592023]
  We propose PVP-Recon, a novel and effective sparse-view surface reconstruction method. PVP-Recon starts initial surface reconstruction with as few as 3 views and progressively adds new views. This progressive view planning progress is interleaved with a neural SDF-based reconstruction module.
  arXiv  Detail & Related papers  (2024-09-09T10:06:34Z)
• ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Field [52.09661042881063]
  We propose an approach that models the bfprovenance for each point -- i.e., the locations where it is likely visible -- of NeRFs as a text field. We show that modeling per-point provenance during the NeRF optimization enriches the model with information on leading to improvements in novel view synthesis and uncertainty estimation.
  arXiv  Detail & Related papers  (2024-01-16T06:19:18Z)
• Gradient-based Local Next-best-view Planning for Improved Perception of Targeted Plant Nodes [0.0]
  We formulate this problem as a local next-best-view (NBV) planning task. Our formulation focuses on quickly improving the perception accuracy of a single target node to maximise its chances of being cut. We propose a gradient-based NBV planner using differential ray sampling, which directly estimates the local gradient direction for viewpoint planning.
  arXiv  Detail & Related papers  (2023-11-28T13:02:33Z)
• LoLep: Single-View View Synthesis with Locally-Learned Planes and Self-Attention Occlusion Inference [66.45326873274908]
  We propose a novel method, LoLep, which regresses Locally-Learned planes from a single RGB image to represent scenes accurately. Compared to MINE, our approach has an LPIPS reduction of 4.8%-9.0% and an RV reduction of 73.9%-83.5%.
  arXiv  Detail & Related papers  (2023-07-23T03:38:55Z)
• Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination [1.4499463058550683]
  We introduce a neural network model based on the Pointnet ++ architecture which makes use of point geometry only. We show that our model outperforms state-of-the-art alternatives on UAV point clouds.
  arXiv  Detail & Related papers  (2023-05-26T14:19:17Z)
• NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction [11.45602594277673]
  We present a novel viewpoint planning approach that explicitly uses information about the predicted fruit shapes to compute targeted viewpoints. We also show the viability of our approach for mapping sweet peppers with a real robotic system in a commercial glasshouse.
  arXiv  Detail & Related papers  (2022-09-30T11:09:54Z)

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.