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FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation

FreeFlow is a bias-free hierarchical transformer achieving state-of-the-art optical flow results on Sintel, KITTI-2015, and Spring benchmarks.

FreeFlow replaces task-specific inductive biases like correlation volumes and iterative warping with a single feed-forward encoder-decoder combining window, shifted-window, and reduced-resolution global attention. It reaches 0.68/1.48 EPE on Sintel Clean/Final, 3.23 Fl-all on KITTI-2015, and 3.192 1px on Spring. The architecture scales consistently from small to large variants and remains memory efficient at 1080p inference.

Hugging Face daily papers · 7d agoAI research

Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation

Marigold V2 adapts diffusion transformers for monocular depth estimation, improving AbsRel 16-26% over the previous best on KITTI and ETH3D.

Huawei's Bayer lab revisits the Marigold approach to repurpose image generation and editing models built on the diffusion transformer (DiT) architecture into monocular depth estimators. The recipes target single-step inference from pretrained multi-step flow-matching models, with remedies including alignment to ground-truth semantic features and a two-stage fine-tuning protocol using a Sinkhorn-based loss. The resulting model produces crisper depth maps that generalize out-of-distribution and also achieves state-of-the-art results on surface normals estimation and intrinsic image decomposition.

Hugging Face daily papers · 9d agoAI research

TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking

Researchers propose TFTrack, a template-free LiDAR 3D single object tracking framework cutting FLOPs ~50% while running at ~120 FPS.

TFTrack is the first template-free framework for 3D Single Object Tracking, dropping template-search pairings and complex motion modeling in favor of the prior bounding box center plus geometric alignment. It ships in three variants (TFTrack-Voxel, TFTrack-Pillar, TFTrack-Point) covering sparse and dense 3D representations. On KITTI and nuScenes it is competitive with leading template-based trackers while reducing FLOPs by about 50% and running near 120 FPS. Code is released, targeting real-time deployment in embedded robotics such as autonomous vehicles.

arXiv cs.AI / cs.LG / cs.CL · 9d agoAI research