Feedforward 3d
- K2F-C-1CroCo: Cross-View Completion Pre-training for 3D VisionAug 2, 2026
CroCo paper note — a self-supervised pretext task tailored to 3D/geometry: mask ~90% of a first image and reconstruct it using the visible patches *plus a second image of the same scene from another viewpoint*, forcing the model to reason about cross-view geometry. Siamese ViT encoder + cross-attention decoder (MAE-style); pre-trained on 1.8M Habitat image pairs; transfers to monocular depth and binocular tasks (flow, relative pose). The direct ancestor of DUSt3R/VGGT
- K2F-C-2CroCo v2: Improved Cross-View Completion Pre-trainingAug 2, 2026
CroCo v2 paper note — scales up cross-view completion pre-training on three axes: (a) millions of **real-world** image pairs (not just synthetic Habitat) via a scalable overlap-controlled collection method, (b) **RoPE** relative position embeddings for robustness to resolution/cropping, (c) a bigger ViT-Large encoder + decoder. Reaches SOTA on stereo matching and optical flow with a plain DPT head and no task-specific tricks; provides the weights DUSt3R initializes from
- K2F-C-3DUSt3R: Geometric 3D Vision Made EasyAug 2, 2026
DUSt3R paper note — the pointmap-regression paradigm: from two uncalibrated images, directly regress two **pointmaps** (per-pixel 3D coords) both expressed in the first image's frame, with no camera poses or intrinsics as input. A Siamese ViT encoder + two cross-attention decoders + DPT heads (initialized from CroCo); a lightweight global alignment fuses many pairs into one scene. Recovers depth, matches, intrinsics, and poses from the pointmaps — replacing the classical SfM/MVS pipeline. The direct precursor of VGGT
- K2F-C-4VGGT: Visual Geometry Grounded TransformerAug 2, 2026
VGGT paper note — a single large (~1.2B) feed-forward transformer that ingests any number of views at once and predicts cameras, depth, pointmaps, and point tracks jointly in one forward pass (seconds), no optimization/global-alignment. Core mechanism: an Alternating-Attention backbone that interleaves frame-wise self-attention and global (cross-view) self-attention — no cross-attention. Beats DUSt3R-line and often optimization-based methods. CVPR 2025 best paper; the endpoint of the arc and the model the xattn-spillover attack targets