Interactive4D: Interactive 4D LiDAR Segmentation

2024-10-10

arXiv.org

AI Summary

Existing LiDAR 4D segmentation methods process frames independently and handle one object per interaction, leading to annotation redundancy, inter-frame inconsistency, and low efficiency. This paper introduces the first interactive 4D LiDAR segmentation paradigm, enabling simultaneous multi-object segmentation over spatiotemporal point cloud sequences. Our core contributions are: (1) a unified 4D interactive segmentation framework that jointly models spatiotemporal geometry and semantics; (2) a LiDAR-optimized click simulation strategy to enhance robustness against sparse, unstructured point clouds; and (3) a multi-frame hypergraph fusion mechanism ensuring consistent instance IDs across frames, seamlessly unifying segmentation and tracking. Extensive experiments on multiple 4D LiDAR benchmarks demonstrate significant improvements over state-of-the-art methods. Code and pretrained models are publicly released.

Technology Category

• Computer Vision: Segmentation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Interactive segmentation has an important role in facilitating the annotation process of future LiDAR datasets. Existing approaches sequentially segment individual objects at each LiDAR scan, repeating the process throughout the entire sequence, which is redundant and ineffective. In this work, we propose interactive 4D segmentation, a new paradigm that allows segmenting multiple objects on multiple LiDAR scans simultaneously, and Interactive4D, the first interactive 4D segmentation model that segments multiple objects on superimposed consecutive LiDAR scans in a single iteration by utilizing the sequential nature of LiDAR data. While performing interactive segmentation, our model leverages the entire space-time volume, leading to more efficient segmentation. Operating on the 4D volume, it directly provides consistent instance IDs over time and also simplifies tracking annotations. Moreover, we show that click simulations are crucial for successful model training on LiDAR point clouds. To this end, we design a click simulation strategy that is better suited for the characteristics of LiDAR data. To demonstrate its accuracy and effectiveness, we evaluate Interactive4D on multiple LiDAR datasets, where Interactive4D achieves a new state-of-the-art by a large margin. We publicly release the code and models at https://vision.rwth-aachen.de/Interactive4D.

Problem

• Enables simultaneous multi-object segmentation across multiple LiDAR scans
• Improves efficiency by leveraging 4D space-time volume for consistent IDs
• Introduces better click simulation for LiDAR data training

Innovation

• Simultaneous multi-object segmentation on multiple LiDAR scans
• Utilizes 4D space-time volume for efficient segmentation
• Improved click simulation for LiDAR data training