Pixel tracking in single-view video sequences has recently emerged as a significant area of research. While previous work has primarily concentrated on tracking within a given video, we propose to expand pixel correspondence estimation into multi-view scenarios. The central concept involves utilizing a canonical space that preserves a universal 3D representation across different views and timesteps. This model allows for precise tracking of points even through prolonged occlusions and significant deformations in appearance between views. Moreover, we show that our model, through the use of an efficient training strategy incorporating distillation loss, is capable of performing incremental pixel tracking, a process often seen as complex in test-time optimization techniques. Comprehensive experiments validate the method's ability to accurately establish point correspondences across cameras. Furthermore, our method achieves promising results of multi-view pixel tracking without requiring the entire video sequences to be provided at once.

Accurate identification and localization of objects in 3-D scenes are essential for advancing comprehensive 3-D scene understanding. Although diffusion models have demonstrated impressive capabilities across a broad spectrum of computer vision tasks, their potential in both 2-D and 3-D object detection remains underexplored. Existing approaches typically formulate detection as a ''noise-to-box'' process, but they rely heavily on direct coordinate regression, which limits adaptability for more advanced tasks such as grounding-based object detection. To overcome these challenges, we propose a promptable 3-D object recognition framework, which introduces a diffusion-based paradigm for flexible and conditionally guided 3-D object detection. Our approach encodes bounding boxes into latent representations and employs latent diffusion models to realize a ''promptable noise-to-box'' transformation. This formulation enables the refinement of standard 3-D object detection using textual prompts, such as class labels. Moreover, it naturally extends to grounding object detection through conditioning on natural language descriptions, and generalizes effectively to few-shot learning by incorporating annotated exemplars as visual prompts. We conduct thorough evaluations on three key 3-D object recognition tasks: general 3-D object detection, few-shot detection, and grounding-based detection. Experimental results demonstrate that our framework achieves competitive performance relative to state-of-the-art methods, validating its effectiveness, versatility, and broad applicability in 3-D computer vision.