SpatioTemporal Difference Network for Video Depth Super-Resolution (Oral)

Zhengxue Wang, Yuan Wu, Xiang Li, Zhiqiang Yan ✉, Jian Yang ✉

• We propose STDNet, a novel framework for video depth super-resolution. STDNet introduces spatial and temporal difference mechanisms to mitigate long-tailed effects in video depth super-resolution. This design enables precise depth calibration and motion compensation, leading to state-of-the-art performance.

See through the Dark: Learning Illumination-affined Representations for Nighttime Occupancy Prediction

Yuan Wu *, Zhiqiang Yan *, Yigong Zhang ✉, Xiang Li, Jian Yang ✉

• Existing vision-based methods perform well on daytime benchmarks but struggle in nighttime scenarios due to limited visibility and challenging lighting conditions. We introduce LIAR, a novel framework that learns illumination-affined representations for nighttime occupacy prediction.

Deep Height Decoupling for Precise Vision-based 3D Occupancy Prediction

Yuan Wu *, Zhiqiang Yan * ✉, Zhengxue Wang, Xiang Li, Le Hui, Jian Yang ✉

• For the first time, we introduce the explicit height prior into the vision-based 3D occupancy predition task. Owing to the novel deep height decoupling and sampling stratagy, our model achieves state-of-the-art performance even with minimal input cost.