Computer Vision News 2 Generating 3D maps of environments is a fundamental task in computer vision. These maps must be actionable, containing crucial information about objects and instances and their positions and relationships to other elements. Recently, the emergence of 3D scene graphs has sparked considerable interest in the field of scene representation. These graphs are easily scalable, updatable, and shareable while maintaining a lightweight, privacy-aware profile. With their increased use in solving downstream tasks, such as navigation, completion, and room rearrangement, this paper explores the potential of leveraging and recycling 3D scene graphs for creating comprehensive 3D maps of environments, a pivotal step in robot-agent operation. “Building 3D scene graphs has recently emerged as a topic in scene representation, which is used in several embodied AI applications to represent the world in a structured and rich manner,” Sayan tells us. “SGAligner focuses on the fundamental problem of aligning pairs of 3D scene graphs whose overlap can range from zero to partial. We address this problem using multimodal learning and leverage the output for multiple downstream tasks of 3D point cloud registration and 3D scene reconstruction by developing a holistic and intuitive understanding of the scene aided with semantic reasoning.” Sayan demonstrates that aligning 3D scenes directly on the scene graph level enhances accuracy and Sayan Deb Sarkar is a Computer Science master’s student at ETH Zurich majoring in Visual Interactive Computing. Currently, he is interning with Qualcomm XR Research in Amsterdam. In this paper, Sayan presents a novel method for aligning pairs of 3D scene graphs robust to in-the-wild scenarios. He speaks to us ahead of his poster this afternoon. SGAligner: 3D Scene Alignment with Scene Graphs ICCV Accepted Paper
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