  Test-Time Training (TTT) proposes to adapt a pre-trained network to changing
data distributions on-the-fly. In this work, we propose the first TTT method
for 3D semantic segmentation, TTT-KD, which models Knowledge Distillation (KD)
from foundation models (e.g. DINOv2) as a self-supervised objective for
adaptation to distribution shifts at test-time. Given access to paired
image-pointcloud (2D-3D) data, we first optimize a 3D segmentation backbone for
the main task of semantic segmentation using the pointclouds and the task of 2D
$\to$ 3D KD by using an off-the-shelf 2D pre-trained foundation model. At
test-time, our TTT-KD updates the 3D segmentation backbone for each test
sample, by using the self-supervised task of knowledge distillation, before
performing the final prediction. Extensive evaluations on multiple indoor and
outdoor 3D segmentation benchmarks show the utility of TTT-KD, as it improves
performance for both in-distribution (ID) and out-of-distribution (ODO) test
datasets. We achieve a gain of up to 13% mIoU (7% on average) when the train
and test distributions are similar and up to 45% (20% on average) when adapting
to OOD test samples.
