3D Scene Understanding Through Local Random Access Sequence Modeling
Authors: name: Wanhee Lee, name: Klemen Kotar, name: Rahul Mysore Venkatesh, name: Jared Watrous, name: Honglin Chen, name: Khai Loong Aw, name: Daniel L. K. Yamins

3D scene understanding from single images is a pivotal problem in computer
vision with numerous downstream applications in graphics, augmented reality,
and robotics. While diffusion-based modeling approaches have shown promise,
they often struggle to maintain object and scene consistency, especially in
complex real-world scenarios. To address these limitations, we propose an
autoregressive generative approach called Local Random Access Sequence (LRAS)
modeling, which uses local patch quantization and randomly ordered sequence
generation. By utilizing optical flow as an intermediate representation for 3D
scene editing, our experiments demonstrate that LRAS achieves state-of-the-art
novel view synthesis and 3D object manipulation capabilities. Furthermore, we
show that our framework naturally extends to self-supervised depth estimation
through a simple modification of the sequence design. By achieving strong
performance on multiple 3D scene understanding tasks, LRAS provides a unified
and effective framework for building the next generation of 3D vision models.