README.md

metadata

license: apache-2.0
library_name: bcdl
tags:
  - rdk-s100
  - rdk-s100p
  - d-robotics
  - bpu
  - hbm
  - depth-estimation

LingBot-Depth for RDK S100/S100P — RGB-D depth refinement

Compiled BPU models (.hbm) for the D-Robotics RDK S100 / S100P, ready to load — no ONNX export, no calibration, no hb_compile. Built and measured with BCDL, a C++17 inference and media library for the RDK S-series with Python bindings.

Upstream: lingbot-depth v0.5 (MDM, DINOv2 ViT-L/14 RGB-D encoder)

Redistributable, including commercially. The licence chain was checked on the code, the pretrained weights it started from, and the data it was trained on — all three, because a permissive repository badge does not by itself say anything about the weights. See Licence.

Files

file	what it is
lingbot_depth_v05_int16_nashm.hbm	all-int16, 480x640 RGB + depth — 1015.3 MB

Measured on an S100P

stage	latency	throughput
refinement	1453 ms	0.69 FPS

hrt_model_exec perf, one thread, minimum of three runs, on a board first gated against its own previously recorded numbers. BPU time only — CPU pre/post-processing is on top and is listed per task in BCDL's benchmark results.

Use it

conda install -c https://mirrors.ruis.ai/conda -c conda-forge bcdl

import bcdl
engine = bcdl.Engine("lingbot_depth_v05_int16_nashm.hbm")
print(engine.input_shape(0), engine.output_shape(0))

Each task has a decoder in BCDL that turns those raw outputs into boxes, keypoints, masks, disparity or text — see the Python API (中文).

What to know before deploying

This refines depth, it does not estimate it. Give it a depth map you already have — stereo, ToF, noisy and full of holes — plus the aligned RGB frame, and it returns hole-filled metric depth with a per-pixel trust mask. It composes with a stereo or depth camera rather than competing with one.

It is a seconds-per-frame model, an order of magnitude slower than anything else here. Use it to refine a keyframe on demand — a grasp pose, a mapping snapshot — not as a stage in a video loop. The cost is the attention score matrix, quadratic in a sequence of 1 + 2N tokens, moving ~23 GB of DDR per frame.

Only the int16 build is published, and int8 is deliberately absent. int8 PTQ does not survive a 24-layer ViT-L: it compiles cleanly and returns a well-formed depth map whose dynamic range has collapsed — 2.9-13.8 m against the float model's 0.97-45.8 m, 233% mean absolute relative error. Publishing it would just be handing somebody a trap.

The deployed graph also keeps every depth token, where upstream drops the ones whose patch holds no valid reading — that masking makes the sequence length depend on the data and cannot be compiled statically. Measured cost of keeping them: 0.06% mean absolute relative error, 0.9999 mask IoU, on scenes that are 87-100% valid. Very sparse input depth was not measured.

Licence

Apache-2.0 on the code and the weights; the DINOv2 backbone is Apache-2.0 too.

BCDL itself is Apache-2.0 and is unrelated to these terms — it is a general-purpose runtime that loads any .hbm. The licence above constrains these weights and this compiled artefact.

The conversion recipe — ONNX export, calibration, hb_compile config and the acceptance numbers — is public in bcdl-model-zoo, so this build can be reproduced or retargeted rather than taken on trust.