For the rest 10k iterations, we further finetune the shared volume and the RGB branch.

Given the super-rich semantics in 3D scenes, a crucial aspect of this task is achieving open-vocabulary segmentation that can handle regions and objects of various semantics including those with long-tail distributions.

Neural Information Processing Systems http://nips.cc/

Moreover, they are primarily collected from limited laboratory scenes with textual descriptions manually labeled, which greatly limits their scalability.

Quality Diversity (QD) is an emerging field in which collections of high performing, behaviorally diverse solutions are trained.

As a case study, we present such a system that learns to parse Newtonian physics problems in textbooks.

For the rest 10k iterations, we further finetune the shared volume and the RGB branch.

Given the super-rich semantics in 3D scenes, a crucial aspect of this task is achieving open-vocabulary segmentation that can handle regions and objects of various semantics including those with long-tail distributions.

Moreover, they are primarily collected from limited laboratory scenes with textual descriptions manually labeled, which greatly limits their scalability.