Interactive4D: Interactive 4D LiDAR Segmentation (2410.08206v2)

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.

• The paper introduces a novel interactive 4D segmentation method that concurrently annotates multiple LiDAR scans to boost efficiency and accuracy.
• It employs a tailored click simulation strategy alongside a sparse convolutional network and refined attention mechanism to optimize user interactions.
• Experimental results on SemanticKITTI and nuScenes show state-of-the-art performance, validating its generalization and reduced annotation effort.

Interactive4D: A New Paradigm for Efficient LiDAR Segmentation

The paper "Interactive4D: Interactive 4D LiDAR Segmentation" presents a novel approach to LiDAR data annotation through interactive 4D segmentation. This work introduces Interactive4D, a model designed to streamline the annotation process by leveraging the sequential nature of LiDAR scans to segment multiple objects across multiple scans simultaneously. The method offers a substantial leap in efficiency and accuracy over existing 3D interactive segmentation techniques.

Key Contributions and Methodology

The primary innovation of this paper is the interactive 4D segmentation paradigm, which allows for simultaneous multi-object segmentation on superimposed LiDAR scans. Unlike conventional methods that segment each object individually in a single scan, Interactive4D processes consecutive scans in unison, exploiting the high-frequency nature of LiDAR data to provide consistent instance IDs over time. This approach not only enhances segmentation efficiency but also simplifies tracking annotations across sequences.

To train the model effectively on LiDAR point clouds, the authors propose a click simulation strategy tailored to the unique properties of LiDAR data. The strategy focuses on selecting error regions in a scale-invariant manner to balance bias towards different object sizes, which is a common challenge due to the sparse nature of LiDAR data. This is achieved by a novel IoU-based metric that aids in identifying significant error regions, and a combination of centroid and randomized click selection strategies to optimize click impact through model training.

The proposed Interactive4D model employs a sparse convolutional network to extract voxel features from the superimposed point clouds and utilizes a refined attention mechanism to incorporate user clicks as refinement cues. This enables the system to iteratively improve segmentation quality with progressive user interactions.

Results and Evaluation

Interactive4D demonstrates substantial improvements in both in-distribution and zero-shot evaluations. When tested on SemanticKITTI, the model achieves state-of-the-art results, significantly surpassing previous methods such as AGILE3D in both 3D and 4D interactive settings. Notably, the model retains high performance on zero-shot testing on the nuScenes dataset, which underlines its generalization capability across different data environments.

The system's effectiveness in reducing the annotation effort is further validated through a user paper, showing that human annotators can achieve high segmentation quality with Interactive4D, comparable to simulated clicks.

Implications and Future Directions

This research provides a significant advancement in the annotation of 3D LiDAR datasets. The interactive 4D segmentation paradigm not only accelerates the labeling process but also establishes a framework that is adaptable to various tracking tasks. By ensuring consistent instance tracking across temporal windows, Interactive4D paves the way for enhancing datasets with robust temporal annotations in autonomous driving and robotics applications.

The paper highlights some limitations, notably in handling very large temporal sequences due to memory constraints and tracking failures over extended periods. Future research can focus on integrating memory-based components or utilizing more advanced data representations to overcome these challenges.

Conclusion

Interactive4D offers an innovative solution to the challenges of LiDAR data annotation, significantly increasing efficiency and accuracy. Through this work, the authors have not only set a new benchmark in interactive segmentation but have also opened up avenues for further research in AI-driven dataset annotation techniques.