Unknown Object Segmentation from Stereo Images

TL;DR

This paper introduces INSTR, a transformer-based method for object instance segmentation from stereo images that does not rely on depth data, enabling effective segmentation in dynamic, cluttered environments.

Contribution

The paper presents a novel stereo image-based instance segmentation approach using transformers, avoiding explicit depth map computation, and demonstrating superior performance over depth-based methods.

Findings

Outperforms state-of-the-art depth-based segmentation methods

Effective in various application domains

Does not require prior object semantic or geometric information

Abstract

Although instance-aware perception is a key prerequisite for many autonomous robotic applications, most of the methods only partially solve the problem by focusing solely on known object categories. However, for robots interacting in dynamic and cluttered environments, this is not realistic and severely limits the range of potential applications. Therefore, we propose a novel object instance segmentation approach that does not require any semantic or geometric information of the objects beforehand. In contrast to existing works, we do not explicitly use depth data as input, but rely on the insight that slight viewpoint changes, which for example are provided by stereo image pairs, are often sufficient to determine object boundaries and thus to segment objects. Focusing on the versatility of stereo sensors, we employ a transformer-based architecture that maps directly from the pair of input images to the object instances. This has the major advantage that instead of a noisy, and potentially incomplete depth map as an input, on which the segmentation is computed, we use the original image pair to infer the object instances and a dense depth map. In experiments in several different application domains, we show that our Instance Stereo Transformer (INSTR) algorithm outperforms current state-of-the-art methods that are based on depth maps. Training code and pretrained models will be made available.