The field of multimodal 3D understanding is rapidly advancing, with a focus on developing models that can effectively reason about 3D spaces from various input sources, including videos and 2D representations. Researchers are exploring innovative approaches to enhance the spatial reasoning capabilities of large multimodal models, such as incorporating 3D visual geometry priors, structured prompting strategies, and novel encoding techniques. These advancements have the potential to improve performance in tasks like 3D question answering, dense captioning, and visual grounding. Notably, some papers have achieved state-of-the-art results without relying on explicit 3D inputs or specialized model architectures.

Particularly noteworthy papers include: S4-Driver, which proposes a scalable self-supervised motion planning algorithm with spatio-temporal visual representation. Learning from Videos for 3D World, which presents a novel method for enhancing multimodal large language models with 3D vision geometry priors. RoboRefer, which introduces a 3D-aware vision-language model that achieves precise spatial understanding and generalized multi-step spatial reasoning.