Papers for

environmental surveyors

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Visibility-aware method improves 3D reconstruction in dense vegetation

Visibility-Aware Reference Generation for Benchmarking Visual 3D Reconstruction in Vegetation-Rich Environments

Abstract: Reliable evaluation of visual 3D reconstruction and depth estimation in vegetation-rich outdoor environments is limited by the scarcity of geometric ground-truth data and the cost of complete scanner surveys. We present a LiDARbased reference-generation pipeline for evaluating the zero-shot performance of feed-forward visual-geometry models alongside conventional structure-from-motion (SfM) methods. The pipeline reuses camera images, LiDAR scans, and reference poses from existing multi-sensor localization datasets to generate per-view depth maps and visible reference point clouds. To address visibility errors in accumulated LiDAR scans, we introduce the LargeDepth-Gap Visibility Score (LDGVS) for point-wise visibility filtering and automatic scale selection in Hidden Point Removal(HPR). Experiments on TartanAirV2 and TartanGround show that, compared with Poisson surface reconstruction, which is an approach suited to completed scan surveys, the combined HPR and LDGVS filtering retains approximately three times the point density at comparable absolute relative error. We also apply the pipeline to the BotanicGarden dataset to systematically compare depth estimation and 3D reconstruction methods. Feedforward methods produce dense depth predictions efficiently, but their 3D reconstructions can contain duplicated structures. Conventional SfM generally produces more coherent geometry, but its reconstructions remain sparse and more prone to failure in challenging vegetated scenes. The benchmark toolkit and datasets will be released.

Tue 3 NovComputer Vision and Pattern RecognitionRobotics
The gist
Evaluating 3D models of environments with lots of plants is hard because it’s expensive to scan everything perfectly. The authors created a new way to use existing camera images and LiDAR scans to generate accurate reference depth maps that take visibility into account. Their method keeps more detailed points than some other approaches, making it easier to compare depth and 3D reconstruction methods. They found that fast feedforward techniques create dense but sometimes messy 3D models, while traditional methods give clearer but sparser results. The authors plan to share their tools and data to help others test similar methods.
Open → 2611.05361v1

Forest point cloud segmentation improved with boundary aware query learning

ForestQuery: Boundary-Aware and Spatially Anchored Query Learning for Unified Forest Point Cloud Segmentation

Abstract: Forest point cloud segmentation is fundamental for fine-grained 3D forest scene understanding, yet remains challenging due to irregular tree structures, severe occlusions, density variations, and ambiguous instance boundaries. Recent query-based forest segmentation methods have shown promise for unified semantic and instance prediction, but they still insufficiently exploit forest-specific spatial structure and account for boundary uncertainty. In this paper, we propose ForestQuery, a boundary-aware and spatially anchored query learning framework for unified forest point cloud segmentation. ForestQuery enhances instance and semantic query learning through two complementary designs. Specifically, boundary uncertainty is explicitly modeled to guide reliable instance query construction and modulate query optimization through adaptive loss reweighting. Meanwhile, spatially anchored semantic query enhancement (SA-SQE) introduces learnable 3D anchors encoding forest vertical stratification priors to enrich semantic queries with explicit spatial references. We evaluate ForestQuery on multiple public forest point cloud benchmarks and a self-collected annotated real-world dataset. Extensive experiments demonstrate consistent improvements in both individual-tree segmentation and semantic segmentation across diverse forest scenes. Code and data are publicly available at https://zhan994.github.io/ForestQuery

Fri 2 OctComputer Vision and Pattern RecognitionArtificial Intelligence
The gist
Understanding complex forest scenes in 3D is hard because tree shapes are irregular and often partly hidden. The authors propose ForestQuery, a new system that better learns to identify individual trees and their types by focusing on uncertain edges and using vertical forest layers as landmarks. They tested it on several datasets, showing it improves how well machines can tell trees apart and label forest parts. This approach helps computers interpret forest environments more accurately.
Open → 2610.03403v1