Agneya Tharun

Reproduced the paper's benchmarks on one NVIDIA L40S GPU. Matched the paper's ViT-B Sintel depth loss and stayed competitive with the 1B-parameter VGGT model on Sintel PC L1 geometric-mean loss (1.635 vs 1.582) at 7× fewer parameters.

Engineered a Pi3/LoGeR-style 5-layer pose head with 9D-rotation SVD orthogonalization to fix the pose-estimation bottleneck, cutting ATE by 53% and beating the published ViT-B baseline on camera pose by 26%.

Vectorized query generation (256× speedup), pre-resized fp16 dataset caches (35× less disk), AMP mixed-precision, and CUDA-allocator tuning enabled full ViT-B training in 46 GB on one consumer GPU. 50 clips/sec at fp16, 3.5 GB peak for inferences.

A three-process system: a phone sender streams frames over secure WebSockets to a FastAPI server with a bounded, drop-on-overflow queue, and a three.js viewer renders the cloud as it grows.

Frames run through Pi3X in 16-frame chunks with 6-frame overlap; the previous chunk's poses, depth, and rays are injected as a prior, and each new chunk is aligned into the global frame by a masked Umeyama SVD.

SAM3 segments prompted objects on keyframes; the 2D masks are lifted into 3D through Pi3X's dense point maps and fitted to oriented bounding boxes, deduplicated across frames and streamed to the viewer.