GBlobs: Local LiDAR Geometry for Improved Sensor Placement Generalization

2025-10-21

AI Summary

LiDAR-based 3D detectors suffer from poor cross-sensor generalization due to reliance on absolute Cartesian coordinates, inducing “geometric shortcuts” that bias models toward sensor-specific pose priors. Method: We propose GBlobs—a local geometric descriptor built around object-centric, rotation-invariant, and scale-normalized point cloud representations. GBlobs explicitly decouples positional priors, compelling the network to focus on intrinsic shape and structural cues rather than absolute coordinates. It replaces raw global coordinates as input while remaining compatible with mainstream 3D detection frameworks. Contribution/Results: Evaluated on Track 3 of the RoboSense 2025 Challenge, GBlobs significantly improves robustness to varying sensor mounting poses and achieves state-of-the-art performance. The approach establishes a novel, interpretable, and transferable paradigm for sensor-agnostic 3D detection.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

This technical report outlines the top-ranking solution for RoboSense 2025: Track 3, achieving state-of-the-art performance on 3D object detection under various sensor placements. Our submission utilizes GBlobs, a local point cloud feature descriptor specifically designed to enhance model generalization across diverse LiDAR configurations. Current LiDAR-based 3D detectors often suffer from a enquote{geometric shortcut} when trained on conventional global features (ie, absolute Cartesian coordinates). This introduces a position bias that causes models to primarily rely on absolute object position rather than distinguishing shape and appearance characteristics. Although effective for in-domain data, this shortcut severely limits generalization when encountering different point distributions, such as those resulting from varying sensor placements. By using GBlobs as network input features, we effectively circumvent this geometric shortcut, compelling the network to learn robust, object-centric representations. This approach significantly enhances the model's ability to generalize, resulting in the exceptional performance demonstrated in this challenge.

Problem

• Addressing geometric shortcut in LiDAR 3D object detection models
• Improving generalization across diverse LiDAR sensor placements
• Replacing position-biased features with robust object-centric representations

Innovation

• GBlobs local point cloud feature descriptor
• Circumvents geometric shortcut in 3D detectors
• Enhances generalization across LiDAR configurations