HiSC4D: Human-centered interaction and 4D Scene Capture in Large-scale Space Using Wearable IMUs and LiDAR (2409.04398v3)

Abstract: We introduce HiSC4D, a novel Human-centered interaction and 4D Scene Capture method, aimed at accurately and efficiently creating a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, rich human-human interactions, and human-environment interactions. By utilizing body-mounted IMUs and a head-mounted LiDAR, HiSC4D can capture egocentric human motions in unconstrained space without the need for external devices and pre-built maps. This affords great flexibility and accessibility for human-centered interaction and 4D scene capturing in various environments. Taking into account that IMUs can capture human spatially unrestricted poses but are prone to drifting for long-period using, and while LiDAR is stable for global localization but rough for local positions and orientations, HiSC4D employs a joint optimization method, harmonizing all sensors and utilizing environment cues, yielding promising results for long-term capture in large scenes. To promote research of egocentric human interaction in large scenes and facilitate downstream tasks, we also present a dataset, containing 8 sequences in 4 large scenes (200 to 5,000 $m^2$), providing 36k frames of accurate 4D human motions with SMPL annotations and dynamic scenes, 31k frames of cropped human point clouds, and scene mesh of the environment. A variety of scenarios, such as the basketball gym and commercial street, alongside challenging human motions, such as daily greeting, one-on-one basketball playing, and tour guiding, demonstrate the effectiveness and the generalization ability of HiSC4D. The dataset and code will be publicated on www.lidarhumanmotion.net/hisc4d available for research purposes.

• The paper presents a novel joint optimization framework that fuses wearable IMU and LiDAR data to accurately capture human motion and complex 4D scenes.
• It combines the high-precision tracking of IMUs with LiDAR’s reliable global localization to overcome sensor limitations and enhance pose smoothness and scene alignment.
• The paper introduces the HiSC4D dataset with 36,000 frames from eight sequences, providing comprehensive annotations to benchmark and advance 4D human-scene capture research.

Insightful Overview of HiSC4D: Human-centered Interaction and 4D Scene Capture

The paper "HiSC4D: Human-centered interaction and 4D Scene Capture in Large-scale Space Using Wearable IMUs and LiDAR" presents an innovative method to address the challenges of capturing dynamic human interactions and environments in large-scale settings. The proposed system, HiSC4D, integrates Inertial Measurement Units (IMUs) and LiDAR technology to ensure precise and efficient 4D scene capture without external references or predefined maps.

Methodology Overview

HiSC4D leverages both IMUs and LiDAR to overcome the limitations inherent in each technology when used independently. While IMUs are advantageous for capturing unrestricted spatial motions, they often suffer from drift over extended periods, leading to inaccuracies. Conversely, LiDAR is reliable for global localization but more coarse in local position estimation. HiSC4D employs a sophisticated joint optimization framework that harmonizes data from these sensors, supplemented by environmental context cues, to deliver stable and accurate human motion capture.

The method involves processing raw IMU and LiDAR data to obtain initial estimates of human motion and scene structure. The system then applies a multi-stage joint optimization process to refine these estimates, incorporating various constraint terms that consider smoothness, self-constraints, scene-aware parameters, and physical interactions. This structured approach not only enhances localization accuracy but also ensures consistent human-scene interactions captured within realistic environments.

Dataset Contribution

To further the research in egocentric human interaction within large scenes, the authors introduce the HiSC4D dataset. This dataset encompasses eight sequences across four varied environments, delivering approximately 36,000 frames of global 4D human motion data. It offers comprehensive SMPL annotations, scene meshes, and a vast collection of cropped human point clouds, making it an invaluable asset for benchmarking and advancing research in human-centered 4D scene capture.

Evaluation and Results

The paper provides a thorough quantitative and qualitative evaluation of the HiSC4D system. The dataset's sequences highlight diverse scenarios and interactions, ranging from sports activities in a gym to guided tours on campuses. The evaluations demonstrate the system's capability to capture accurate human motions and interactions in expansive settings. Quantitative assessments indicate significant improvements in pose smoothness, localization accuracy, and scene alignment compared to baseline methods relying solely on IMU or LiDAR data.

Practical and Theoretical Implications

The practical implications of HiSC4D are substantial, offering a flexible and accessible solution for capturing human dynamics in real-world environments. Potential applications span autonomous driving, augmented reality, robotics, and social behavior analysis, where understanding nuanced human interactions within complex scenes is crucial.

Theoretically, HiSC4D's novel joint optimization framework and dataset are poised to drive advancements in 3D computer vision, enhancing our understanding of motion capture and scene reconstruction. The approach could inspire future research within AI to explore more robust and comprehensive methods of integrating multiple sensor modalities for diverse applications.

Future Prospects

Moving forward, the incorporation of modalities like RGB video could address existing limitations in vertical coverage and resolution, presenting avenues for developing richer and more detailed representations of human interactions. Additionally, addressing computational complexity will be critical for extending the system's use in real-time and large-scale applications.

In conclusion, HiSC4D makes notable contributions to the field of human-centered scene capture, laying the groundwork for diverse real-world applications and future explorations in AI-centered interaction and 4D scene mapping. The paper represents a methodical and comprehensive approach toward achieving dynamic and context-sensitive scene capture in large-scale environments.