Scalable 3D Benchmark

Scalable 3D benchmarks are crucial for evaluating and advancing the performance of 3D processing models, particularly in areas like multi-modal large language models and computer vision. Current research focuses on developing benchmarks that encompass diverse tasks and scales, from object-level recognition to scene-level understanding, often employing transformer-based architectures and diffusion models for tasks such as point cloud registration, 3D object tracking, and surface reconstruction. These benchmarks are vital for objectively assessing progress in 3D data processing, driving improvements in areas ranging from autonomous driving and robotics to medical image analysis and digital asset protection.