Event-Driven Dynamic Scene Depth Completion
Zhiqiang Yan, Jianhao Jiao, Zhengxue Wang, Gim Hee Lee
Depth completion in dynamic scenes poses significant challenges due to rapid ego-motion and object motion, which can severely degrade the quality of input modalities such as RGB images and LiDAR measurements. Conventional RGB-D sensors often struggle to align precisely and capture reliable depth under such conditions. In contrast, event cameras, with their high temporal resolution and sensitivity to motion at the pixel level, provide complementary cues that are particularly beneficial in dynamic environments. To this end, we propose EventDC, the first event-driven depth completion framework. It consists of two key components: Event-Modulated Alignment (EMA) and Local Depth Filtering (LDF). Both modules adaptively learn the two fundamental components of convolution operations: offsets and weights conditioned on motion-sensitive event streams. In the encoder, EMA leverages events to modulate the sampling positions of RGB-D features to achieve pixel redistribution for improved alignment and fusion. In the decoder, LDF refines depth estimations around moving objects by learning motion-aware masks from events. Additionally, EventDC incorporates two loss terms to further benefit global alignment and enhance local depth recovery. Moreover, we establish the first benchmark for event-based depth completion, comprising one real-world and two synthetic datasets, to facilitate future research. Extensive experiments on this benchmark demonstrate the superiority of EventDC. Our code and dataset will be released on paper acceptance.