5 accepted papers
Neural video compression (NVC) has demonstrated superior compression efficiency, yet effective rate control remains a significant challenge due to complex temporal dependencies. Existing rate control schemes typically leverage frame content to capture distortion interactions, overlooking inter-frame
In autonomous driving, mapping is critical for motion planning but remains an under-utilized resource for perception tasks like 3D object detection. Maps can provide robust structural priors of the static environment, suited to resolving ambiguities and correcting for sensor data sparsity or noise -
We propose a novel adaptive prediction network that dynamically determines the optimal sampling factor and Lagrangian multiplier for encoding each frame, guided by sequential information. By exploiting spatio-temporal redundancy through adaptive sampling, our method reduces bitrate consumption while…
Motion compensation is a key technology in video coding for removing the temporal redundancy between video frames. Considering the incompatibility between traditional interpolation filters and diversified video content, the inter prediction method still has considerable room for improvement. This pa…
In this paper, we propose a graph neural network to detect objects from a LiDAR point cloud. Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors graph. We design a graph neural network, named Point-GNN, to predict the category and shape of the object that each ve…