Consistency-aware Self-Training for Iterative-based Stereo Matching
CST-Stereo achieves impressive results in various scenarios including in-domain, domain adaptive and domain generalization.
CST-Stereo achieves impressive results in various scenarios including in-domain, domain adaptive and domain generalization.
A multi-modal large model focusing on geometric understanding and reasoning tasks with formalized visual-language pre-training.
AIOStereo flexibly selects and transfers knowledge from multiple heterogeneous VFMs to a single stereo matching model. Rank 1st on Middlebury Stereo Evaluation.
AdaptiveDiffusion adaptively reduces noise prediction steps during denoising guided by third-order latent difference.
Exploit the potential of Mamba architecture on 3D scene-level perception for the first time.
A pseudo-label-based test-time adaptive 3D object detection method exploring a new task of test-time domain adaptive 3D detection.
A Reconstruction-Simulation-Perception scheme for alleviating domain shifts in autonomous driving.
Build a large-scale pre-training point-cloud dataset with diverse data distribution, and learn generalizable representations.
A unified baseline to tackle multi-dataset 3D object detection from data-level and semantic-level.
A Bi-domain active learning approach which selects samples from both source and target domain to solve cross-domain 3D object detection.