Counting Stacked Objects from Multi-View Images

Visual object counting is a fundamental computer vision task underpinning numerous real-world applications, from cell counting in biomedicine to traffic and wildlife monitoring. However, existing methods struggle to handle the challenge of stacked 3D objects in which most objects are hidden by those above them. To address this important yet underexplored problem, we propose a novel 3D counting approach that decomposes the task into two complementary subproblems - estimating the 3D geometry of the object stack and the occupancy ratio from multi-view images. By combining geometric reconstruction and deep learning-based depth analysis, our method can accurately count identical objects within containers, even when they are irregularly stacked. We validate our 3D Counting pipeline on diverse real-world and large-scale synthetic datasets, which we will release publicly to facilitate further research.

DiscoNeRF: Class-Agnostic Object Field for 3D Object Discovery

Neural Radiance Fields (NeRFs) have become a powerful tool for modeling 3D scenes from multiple images. However, NeRFs remain difficult to segment into semantically meaningful regions. Previous approaches to 3D segmentation of NeRFs either require user interaction to isolate a single object, or they rely on 2D semantic masks with a limited number of classes for supervision. As a consequence, they generalize poorly to class-agnostic masks automatically generated in real scenes. This is attributable to the ambiguity arising from zero-shot segmentation, yielding inconsistent masks across views. In contrast, we propose a method that is robust to inconsistent segmentations and successfully decomposes the scene into a set of objects of any class. By introducing a limited number of competing object slots against which masks are matched, a meaningful object representation emerges that best explains the 2D supervision and minimizes an additional regularization term. Our experiments demonstrate the ability of our method to generate 3D panoptic segmentations on complex scenes, and extract high-quality 3D assets from NeRFs that can then be used in virtual 3D environments.