UniSplat: Unified Spatio-Temporal Fusion via 3D Latent Scaffolds for Dynamic Driving Scene Reconstruction

A new study introduces Semantic Orthogonal Calibration (SoC), a method aimed at improving the calibration of uncertainty estimates in vision-language models (VLMs) during test-time prompt tuning. This approach addresses the challenge of overconfidence in models by enforcing smooth prototype separation while maintaining semantic proximity.

A recent survey on learning-based Multi-View Stereo (MVS) techniques highlights the advancements in 3D reconstruction, which is crucial for applications such as Augmented and Virtual Reality, autonomous driving, and robotics. The study categorizes these methods into depth map-based, voxel-based, NeRF-based, and others, emphasizing the effectiveness of depth map-based approaches.

The landscape of video generation is evolving, transitioning from merely creating visually appealing clips to constructing interactive virtual environments that adhere to physical plausibility. This shift is highlighted in a recent survey that conceptualizes modern video foundation models as a combination of implicit world models and video renderers, enabling coherent visual reasoning and task planning.