SceneDiff: A Benchmark and Method for Multiview Object Change Detection

We investigate the problem of identifying objects that have been added, removed, or moved between a pair of captures (images or videos) of the same scene at different times. Detecting such changes is important for many applications, such as robotic tidying or construction progress and safety monitoring. A major challenge is that varying viewpoints can cause objects to falsely appear changed. We introduce SceneDiff Benchmark, the first multiview change detection benchmark with object instance annotations, comprising 350 diverse video pairs with thousands of changed objects. We also introduce the SceneDiff method, a new training-free approach for multiview object change detection that leverages pretrained 3D, segmentation, and image encoding models to robustly predict across multiple benchmarks. Our method aligns the captures in 3D, extracts object regions, and compares spatial and semantic region features to detect changes. Experiments on multi-view and two-view benchmarks demonstrate that our method outperforms existing approaches by large margins (94% and 37.4% relative AP improvements). The benchmark and code will be publicly released.

LidarDM: Generative LiDAR Simulation in a Generated World

We present LidarDM, a novel LiDAR generative model capable of producing realistic, layout-aware, physically plausible, and temporally coherent LiDAR videos. LidarDM stands out with two unprecedented capabilities in LiDAR generative modeling: (i) LiDAR generation guided by driving scenarios, offering significant potential for autonomous driving simulations, and (ii) 4D LiDAR point cloud generation, enabling the creation of realistic and temporally coherent sequences. At the heart of our model is a novel integrated 4D world generation framework. Specifically, we employ latent diffusion models to generate the 3D scene, combine it with dynamic actors to form the underlying 4D world, and subsequently produce realistic sensory observations within this virtual environment. Our experiments indicate that our approach outperforms competing algorithms in realism, temporal coherency, and layout consistency. We additionally show that LidarDM can be used as a generative world model simulator for training and testing perception models.