LiDAR-Event Stereo Fusion with Hallucinations (2408.04633v1)

Abstract: Event stereo matching is an emerging technique to estimate depth from neuromorphic cameras; however, events are unlikely to trigger in the absence of motion or the presence of large, untextured regions, making the correspondence problem extremely challenging. Purposely, we propose integrating a stereo event camera with a fixed-frequency active sensor -- e.g., a LiDAR -- collecting sparse depth measurements, overcoming the aforementioned limitations. Such depth hints are used by hallucinating -- i.e., inserting fictitious events -- the stacks or raw input streams, compensating for the lack of information in the absence of brightness changes. Our techniques are general, can be adapted to any structured representation to stack events and outperform state-of-the-art fusion methods applied to event-based stereo.

• The paper introduces novel "hallucination" methods to improve event-based stereo depth estimation by injecting synthetic events guided by sparse LiDAR data.
• Two techniques, Virtual Stack Hallucination (VSH) and Back-in-Time Hallucination (BTH), augment data streams with fictitious information to address sparsity and asynchronous sensor timing.
• Evaluations on DSEC and M3ED datasets demonstrate that the proposed methods significantly outperform existing LiDAR-event fusion approaches while preserving microsecond resolution.

LiDAR-Event Stereo Fusion with Hallucinations

The paper "LiDAR-Event Stereo Fusion with Hallucinations" by Bartolomei et al. explores innovative methodologies to enhance event-based stereo depth estimation by integrating sparse depth hints from LiDAR sensors. Depth estimation is integral to various applications such as autonomous vehicles and 3D reconstruction, posing challenges in dynamic environments with varying motion and light conditions.

Contributions and Methodology

The paper introduces a novel approach termed hallucination, where it proposes injecting fictitious events into the stereo data to overcome the inherent sparsity and semi-density issues in event streams. This is particularly relevant for conditions absent of dynamic changes, where traditional event cameras may struggle by offering sparse and incomplete information.

1. Virtual Stack Hallucination (VSH): This approach involves augmenting stereo data stacks with artificially generated stacks, effectively enhancing the feature space for the stereo matching. The method synthesizes patterns in the stacks at positions informed by LiDAR depth cues, which are projected back into the disparity domain. This method leverages the internal architectures of stereo models, such as histograms or voxel grids, without necessarily modifying their core functionality, ensuring that it is adaptable across various stereo processing pipelines.
2. Back-in-Time Hallucination (BTH): This technique operates directly in the temporal domain of the event history. It introduces synthetic events that align temporally with LiDAR-derived depth points. These are injected between asynchronous reads of LiDAR and event cameras, using fictional timestamps and polarities to simulate continuous changes. The temporal integration facilitates depth estimation without constraints on synchronous data acquisition, enabling continuous microsecond-resolution processing native to event cameras.

Evaluation and Results

The proposed methods are rigorously benchmarked against existing fusion methodologies that use LiDAR data, such as Guided Stereo Matching and concatenation-based approaches. Their evaluation includes two key datasets:

• DSEC Dataset: An outdoor dataset that utilizes a 16-line LiDAR and 640x480 resolution event cameras.
• M3ED Dataset: It comprises scenes from multi-sensor fusion testbeds using a 64-line LiDAR with higher resolution event cameras.

Results on these datasets indicate that both VSH and BTH outperform traditional methodologies by significant margins, especially when the event camera data is sparse or misaligned with the LiDAR input. Notably, the methods preserved the advantages of microsecond-level event camera resolution even with the slower sampling rates typical of LiDAR systems.

Implications and Future Research

The techniques elucidated offer compelling improvements over extant fusion approaches by resolving the asynchronous data acquisition challenge through intelligent data augmentation strategies. Future developments could explore adaptive mechanisms where the balance between injected synthetic data and real captured data is dynamically tuned based on environmental context or sensor feedback loops. Additionally, such hallucination strategies could be applied or adapted beyond stereo vision, potentially benefiting other sensor fusion paradigms.

This paper delineates a practical path forward in optimizing depth estimation systems in real-world conditions. It suggests future explorations in leveraging further advancements in deep learning architectures that can inherently incorporate hallucination mechanisms, possibly exploring generative models to synthesize plausible, context-aware events.