Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems

• URL: http://arxiv.org/abs/2506.06995v1
• Date: Sun, 08 Jun 2025 04:55:44 GMT
• Title: Technical Report for ICRA 2025 GOOSE 3D Semantic Segmentation Challenge: Adaptive Point Cloud Understanding for Heterogeneous Robotic Systems
• Abstract summary: The challenge focuses on semantic segmentation of 3D point clouds from diverse unstructured outdoor environments collected from robotic platforms.<n>This problem was addressed by implementing Point Prompt Tuning integrated with Point Transformer v3.<n>The approach achieved substantial performance improvements with mIoU increases of up to 22.59% on challenging platforms.
• Score: 3.090529106242154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This technical report presents the implementation details of the winning solution for the ICRA 2025 GOOSE 3D Semantic Segmentation Challenge. This challenge focuses on semantic segmentation of 3D point clouds from diverse unstructured outdoor environments collected from multiple robotic platforms. This problem was addressed by implementing Point Prompt Tuning (PPT) integrated with Point Transformer v3 (PTv3) backbone, enabling adaptive processing of heterogeneous LiDAR data through platform-specific conditioning and cross-dataset class alignment strategies. The model is trained without requiring additional external data. As a result, this approach achieved substantial performance improvements with mIoU increases of up to 22.59% on challenging platforms compared to the baseline PTv3 model, demonstrating the effectiveness of adaptive point cloud understanding for field robotics applications.

Related papers

• RegFormer++: An Efficient Large-Scale 3D LiDAR Point Registration Network with Projection-Aware 2D Transformer [71.59462491691819]
  We propose a novel end-to-end differential transformer network, termed RegFormer++, for large-scale point cloud alignment.<n>Because we fill original 3D coordinates into 2D projected positions, our designed transformer can benefit from both high efficiency in 2D processing and accuracy from 3D geometric information.<n>Experiments on KITTI, NuScenes, and Argoverse datasets demonstrate that our model achieves state-of-the-art performance in terms of both accuracy and efficiency.
  arXiv  Detail & Related papers  (2026-03-15T09:03:10Z)
• Towards Cross-Platform Generalization: Domain Adaptive 3D Detection with Augmentation and Pseudo-Labeling [58.42010583822351]
  This technical report represents the award-winning solution to the Cross-platform 3D Object Detection task in the RoboSense2025 Challenge.<n>Our approach is built upon PVRCNN++, an efficient 3D object detection framework that effectively integrates point-based and voxel-based features.<n>We improve cross-platform generalization by narrowing domain gaps through tailored data augmentation and a self-training strategy with pseudo-labels.
  arXiv  Detail & Related papers  (2026-01-13T03:09:20Z)
• A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features [60.76577388438418]
  3D anomaly detection (AD) is a crucial task in computer vision, aiming to identify anomalous points or regions from point cloud data.<n>Existing methods may encounter challenges when handling point clouds with changes in orientation and position because the resulting features may vary significantly.<n>We propose a novel Rotationally Invariant Features (RIF) framework for 3D AD, which maps each point into a rotationally invariant space to maintain consistency of representation.
  arXiv  Detail & Related papers  (2025-11-17T08:16:05Z)
• Empowering Bridge Digital Twins by Bridging the Data Gap with a Unified Synthesis Framework [5.498306044171154]
  This paper proposes a systematic framework for generating 3D bridge data.<n>It can automatically generate point clouds featuring component-level instance annotations, high-fidelity color, and precise normal vectors.<n> Experiments demonstrate that a PointNet++ model trained with our synthetic data achieves a mean Intersection over Union (mIoU) of 84.2% in real-world bridge semantic segmentation.
  arXiv  Detail & Related papers  (2025-07-08T09:34:55Z)
• SPPSFormer: High-quality Superpoint-based Transformer for Roof Plane Instance Segmentation from Point Clouds [14.67024375365087]
  Transformers have been seldom employed in point cloud roof plane instance segmentation.<n>Existing superpoint Transformers suffer from limited performance due to the use of low-quality superpoints.<n>We establish two criteria that high-quality superpoints should satisfy and introduce a corresponding two-stage superpoint generation process.
  arXiv  Detail & Related papers  (2025-05-30T11:23:16Z)
• FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation [52.89847760590189]
  3D scene understanding is a critical yet challenging task in autonomous driving.<n>Recent methods leverage the range-view representation to improve processing efficiency.<n>We re-design the workflow for range-view-based LiDAR semantic segmentation.
  arXiv  Detail & Related papers  (2025-02-13T12:39:26Z)
• Point Transformer V3 Extreme: 1st Place Solution for 2024 Waymo Open Dataset Challenge in Semantic Segmentation [98.11452697097539]
  In this technical report, we detail our first-place solution for the 2024 Open dataset Challenge's semantic segmentation track. We significantly enhanced the performance of Point Transformer V3 on the benchmark by implementing cutting-edge, plug-and-play training and inference technologies. This approach secured us the top position on the Open dataset segmentation leaderboard, markedly outperforming other entries.
  arXiv  Detail & Related papers  (2024-07-21T22:08:52Z)
• 3D Adaptive Structural Convolution Network for Domain-Invariant Point Cloud Recognition [3.3748750222488657]
  3D Adaptive Structural Convolution Network (3D-ASCN) is a cutting-edge framework for 3D point cloud recognition. It combines 3D convolution kernels, a structural tree structure, and adaptive neighborhood sampling for effective geometric feature extraction.
  arXiv  Detail & Related papers  (2024-07-05T19:38:10Z)
• Adaptive Point Transformer [88.28498667506165]
  Adaptive Point Cloud Transformer (AdaPT) is a standard PT model augmented by an adaptive token selection mechanism. AdaPT dynamically reduces the number of tokens during inference, enabling efficient processing of large point clouds.
  arXiv  Detail & Related papers  (2024-01-26T13:24:45Z)
• Learning-Based Biharmonic Augmentation for Point Cloud Classification [79.13962913099378]
  Biharmonic Augmentation (BA) is a novel and efficient data augmentation technique. BA diversifies point cloud data by imposing smooth non-rigid deformations on existing 3D structures. We present AdvTune, an advanced online augmentation system that integrates adversarial training.
  arXiv  Detail & Related papers  (2023-11-10T14:04:49Z)
• AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers [94.11915008006483]
  We present a new method that reformulates point cloud completion as a set-to-set translation problem. We design a new model, called PoinTr, which adopts a Transformer encoder-decoder architecture for point cloud completion. Our method attains 6.53 CD on PCN, 0.81 CD on ShapeNet-55 and 0.392 MMD on real-world KITTI.
  arXiv  Detail & Related papers  (2023-01-11T16:14:12Z)
• Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation [78.6612285236938]
  We propose a novel DAT (textbfDual textbfAdaptive textbfTransformations) model for weakly supervised point cloud segmentation. We evaluate our proposed DAT model with two popular backbones on the large-scale S3DIS and ScanNet-V2 datasets.
  arXiv  Detail & Related papers  (2022-07-19T05:43:14Z)

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.