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Update to 2026-09-17 pipeline snapshot; add weights, L20 assets and recording via Git LFS

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Source: RGB-D -> Dyn-HaMR -> L20 retargeting -> FoundationPose -> reference repair -> SPIDER,
documented in docs/PIPELINE_LATEST.md and docs/SETUP_AND_WEIGHTS.md. Adds FoundationPose and
nvdiffrast upstream snapshots, requirements/pipeline_venv.txt and the FoundationPose weight
manifest/downloader.

Assets (Git LFS): weights/ (WiLoR detector, HandFlow denoiser, UniDepth-L), FoundationPose
checkpoints, HaMeR checkpoint, Dyn-HaMR HMP model and BMC constraints, L20 URDF/meshes, the
20260915_171525 D405 recording and the two box CADs. MANO models are not redistributed
(third_party/hamer/_DATA/data/mano/README.txt). Environments, caches and run outputs excluded.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
This commit is contained in:
liyang
2026-09-17 11:27:46 +08:00
parent 7e4ef6f98b
commit ae28d55f81
761 changed files with 55598 additions and 59 deletions
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third_party/nvdiffrast/samples/torch/triangle.py
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import imageio
import numpy as np
import torch
import nvdiffrast.torch as dr
def tensor(*args, **kwargs):
return torch.tensor(*args, device='cuda', **kwargs)
glctx = dr.RasterizeCudaContext()
pos = tensor([[[-0.8, -0.8, 0, 1], [0.8, -0.8, 0, 1], [-0.8, 0.8, 0, 1]]], dtype=torch.float32)
col = tensor([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]], dtype=torch.float32)
tri = tensor([[0, 1, 2]], dtype=torch.int32)
rast, _ = dr.rasterize(glctx, pos, tri, resolution=[256, 256])
out, _ = dr.interpolate(col, rast, tri)
img = out.cpu().numpy()[0, ::-1, :, :] # Flip vertically.
img = np.clip(np.rint(img * 255), 0, 255).astype(np.uint8) # Quantize to np.uint8
print("Saving to 'tri.png'.")
imageio.imsave('tri.png', img)