Perspective-Invariant 3D Object Detection

2025-07-23

AI Summary

Existing LiDAR-based 3D detection methods are primarily designed for vehicular platforms and suffer from strong viewpoint dependency and poor cross-platform generalizability—especially on heterogeneous autonomous systems such as quadrupedal robots and UAVs. To address this, we introduce Pi3DET, the first multi-platform LiDAR 3D detection benchmark, and propose a viewpoint-invariant unified detection framework. Our approach jointly models geometric and feature alignment: geometric alignment normalizes coordinate systems and decouples scale across platforms, while feature alignment employs a cross-domain adaptive attention mechanism to bridge domain gaps. This enables effective knowledge transfer among vehicles, quadrupeds, and UAVs. Extensive experiments demonstrate that our method achieves significantly higher mean average precision (mAP) than state-of-the-art methods under cross-platform evaluation settings, validating its robustness and generalizability in complex real-world scenarios. Pi3DET establishes a new paradigm for universal 3D perception systems.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

With the rise of robotics, LiDAR-based 3D object detection has garnered significant attention in both academia and industry. However, existing datasets and methods predominantly focus on vehicle-mounted platforms, leaving other autonomous platforms underexplored. To bridge this gap, we introduce Pi3DET, the first benchmark featuring LiDAR data and 3D bounding box annotations collected from multiple platforms: vehicle, quadruped, and drone, thereby facilitating research in 3D object detection for non-vehicle platforms as well as cross-platform 3D detection. Based on Pi3DET, we propose a novel cross-platform adaptation framework that transfers knowledge from the well-studied vehicle platform to other platforms. This framework achieves perspective-invariant 3D detection through robust alignment at both geometric and feature levels. Additionally, we establish a benchmark to evaluate the resilience and robustness of current 3D detectors in cross-platform scenarios, providing valuable insights for developing adaptive 3D perception systems. Extensive experiments validate the effectiveness of our approach on challenging cross-platform tasks, demonstrating substantial gains over existing adaptation methods. We hope this work paves the way for generalizable and unified 3D perception systems across diverse and complex environments. Our Pi3DET dataset, cross-platform benchmark suite, and annotation toolkit have been made publicly available.

Problem

• Addressing lack of 3D object detection for non-vehicle platforms
• Proposing cross-platform adaptation for perspective-invariant 3D detection
• Evaluating robustness of 3D detectors in cross-platform scenarios

Innovation

• Introduces Pi3DET benchmark for multi-platform LiDAR data
• Proposes cross-platform adaptation framework for 3D detection
• Achieves perspective-invariant detection via geometric feature alignment