NVDS_onnx

NVDS Temporal Depth Stabilizer ONNX

This document describes the NVDS (Neural Video Depth Stabilizer) ONNX export used by PTMediaServer as an optional temporal depth stabilizer in the offline 2D-to-3D/VR pipeline.

The exporter is examples/export_nvds_onnx.py. It converts the NVDS PyTorch checkpoint into fixed-resolution ONNX Runtime graphs so inference does not need PyTorch at runtime. NVDS smooths the per-frame depth that DA3 produces, removing the temporal "depth swimming" that causes discomfort when the stereo output is viewed.

Upstream

• Official project: https://github.com/RaymondWang987/NVDS
• This exporter targets the original NVDS_Stabilizer.pth checkpoint (a SegFormer MiT-B5 backbone plus a focal cross-attention stabilizer head).
• These ONNX files are derived from NVDS weights and should be used according to the upstream license and citation requirements.

Files And Paths

examples/export_nvds_onnx.py
examples/nvds_README.md
models/NVDS/NVDS_Stabilizer.pth            (input checkpoint, ~354 MB)
models/NVDS/NVDS_Stabilizer_672x384.onnx   (monolithic export)
models/NVDS/NVDS_Backbone_512x288.onnx     (split: per-frame backbone)
models/NVDS/NVDS_Head_512x288.onnx         (split: cross-frame head)
models/NVDS/NVDS_Backbone_672x384.onnx
models/NVDS/NVDS_Head_672x384.onnx

The conversion script defaults are currently:

Resolution tiers PTMediaServer uses (both 16:9):

Export Modes

The exporter has two modes.

Monolithic (--width/--height, no --split)

A single graph that takes the whole 4-frame window and runs the backbone on all 4 frames internally. Output name NVDS_Stabilizer_{w}x{h}.onnx.

Split (--split, recommended)

Two graphs that let the runtime run the heavy backbone once per frame and cache the last 4 results, instead of recomputing 3/4 of it for every sliding window:

Both modes are numerically identical (the backbone has no cross-frame ops, so its features are batch-independent). Measured max_abs_diff between split and monolith is about 3e-05.

Note on performance: the focal cross-attention head, not the backbone, dominates NVDS cost, so the split's main practical benefit is enabling the lower-resolution tier (the head cost scales with input pixel count). See summary/summary_20260620_NVDS_INTEGRATION_EXTERNAL_REVIEW_CN.md for the measurements.

Fixed Resolution

Each ONNX file is exported at a fixed input resolution.

Rules:

• Height and width are fixed in each ONNX file.
• --width and --height must each be a multiple of 32.
• The intended tiers are 512 x 288 and 672 x 384 (16:9). Re-export to add another size.

Input And Output

Monolithic graph

input name : rgbd_seq
input dtype: float32
input shape: [1, 4, 4, height, width]   (batch, time=4, channels=RGB+near, H, W)

output name : stabilized_depth
output dtype: float32
output shape: [1, 1, height, width]

Split graphs

Backbone:

input name : frame_rgbd
input shape: [1, 4, height, width]       (one RGBD frame: RGB + near)

output names: feat0, feat1, feat2, feat3 (multi-scale features, batch 1)

Head:

input names: feat0..feat3                (each [4, C, h, w] = the 4 window frames stacked)
             last_rgb                     ([1, 3, height, width], the current frame's RGB)

output name : stabilized_depth
output shape: [1, 1, height, width]

The output is a stabilized normalized near/disparity map (larger = nearer), already temporally smoothed. Unlike raw DA3 depth, it must NOT be reciprocated or percentile-normalized again at render time; PTMediaServer feeds it through the dedicated render_near(...) path.

Preprocessing

For every frame, the runtime wrapper builds a 4-channel RGBD frame at the export resolution:

1. Load the frame as RGB and resize to [width, height].
2. Convert to float32 in 0.0 .. 1.0 and apply ImageNet normalization with mean [0.485, 0.456, 0.406] and std [0.229, 0.224, 0.225]; transpose to CHW.
3. Convert the DA3 depth to a normalized near/disparity map (reciprocal + percentile normalization) and resize it to [width, height].
4. Concatenate RGB (3 ch) + near (1 ch) into a [4, height, width] frame.

For the monolithic graph the wrapper keeps the last 4 such frames, padding the first frames by repetition, and stacks them into [1, 4, 4, H, W]. For the split graphs it runs the backbone on each frame, caches the last 4 feature tuples, stacks them on the batch axis, and passes the current frame's RGB as last_rgb.

PTMediaServer's offline.nvds_stabilizer.NvdsDepthStabilizer performs all of this and auto-selects the split graphs when they are present.

Runtime Dependencies

CPU:

pip install onnxruntime numpy opencv-python

GPU:

pip install onnxruntime-gpu numpy opencv-python

TensorRT is not usable for NVDS (see Notes); the runtime uses the CUDA execution provider with a bounded GPU memory arena.

Quick Inference Example

This example runs the split graphs over a 4-frame window. frame_rgbd_t is a preprocessed [1, 4, H, W] float32 frame as described in Preprocessing.

import numpy as np
import onnxruntime as ort

W, H = 512, 288
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
backbone = ort.InferenceSession("models/NVDS/NVDS_Backbone_512x288.onnx", providers=providers)
head = ort.InferenceSession("models/NVDS/NVDS_Head_512x288.onnx", providers=providers)

bb_out = [o.name for o in backbone.get_outputs()]          # feat0..feat3
window = []                                                # last 4 feature tuples
for frame_rgbd_t in stream:                                # each [1, 4, H, W] float32
    feats = backbone.run(bb_out, {"frame_rgbd": frame_rgbd_t})
    window.append(feats)
    window = window[-4:]
    pad = [window[0]] * (4 - len(window)) + window         # causal padding
    feeds = {f"feat{s}": np.concatenate([pad[t][s] for t in range(4)], axis=0)
             for s in range(4)}
    feeds["last_rgb"] = np.ascontiguousarray(frame_rgbd_t[:, 0:3])
    stable_near = head.run(["stabilized_depth"], feeds)[0][0, 0]   # [H, W]

Convert From PyTorch Weights

Run the exporter from this repository root. The VR_Video_Toolbox_NE virtual environment already contains the PyTorch dependencies (the runtime venv is ONNX-only and has no torch):

G:/GIT/debug/VR_Video_Toolbox_NE/.venv/Scripts/python.exe \
  examples/export_nvds_onnx.py --split --width 512 --height 288 --device cuda

Export the high-quality tier:

python examples/export_nvds_onnx.py --split --width 672 --height 384 --device cuda

Export the monolithic graph instead of the split pair:

python examples/export_nvds_onnx.py --width 672 --height 384 --device cuda

Useful options:

--split               Export backbone + head graphs instead of the monolith.
--width 672           Input width. Must be a multiple of 32.
--height 384          Input height. Must be a multiple of 32.
--source-root PATH    Vendored NVDS source root (contains full_model.py).
--checkpoint PATH     NVDS_Stabilizer.pth.
--output PATH         Output path for the monolithic export.
--opset 17            ONNX opset version.
--device cpu|cuda     Device used for tracing.
--dynamic-batch       Mark only the batch axis dynamic (monolithic only).
--skip-ort-check      Skip the ONNX Runtime comparison.

Expected checkpoint layout:

NVDS/
  NVDS_Stabilizer.pth

Validation

The exporter compares outputs with ONNX Runtime and reports a max/mean absolute difference. In --split mode it runs the per-frame backbone plus head over a 4-frame window and compares against the monolithic PyTorch forward, so a single run verifies that the split is equivalent to the original model.

Notes

• TensorRT cannot run this graph: the optimized model exceeds the 2 GB protobuf limit and the graph contains many ScatterND / dynamic ops. The runtime treats a trt request for NVDS as CUDA and bounds the CUDA arena with gpu_mem_limit (DA3 still uses TensorRT normally).
• NVDS is VRAM-heavy. Sharing the GPU with DA3 and the renderer pushes the combined working set close to 16 GB; the bounded arena keeps it from spilling into Windows shared memory.
• NVDS is limited to 16:9 input in this project.
• The output is a stabilized near/disparity map and must be rendered through the render_near(...) path, not the raw-depth path.
• The split graphs are preferred and auto-detected at runtime; the monolith is a fallback when the split files are absent.

Citation

If you use NVDS or ONNX exports derived from it, cite the upstream work (verify against the upstream repository for the authoritative entry):

@InProceedings{Wang_2023_ICCV,
  author    = {Wang, Yiran and Pan, Zhiyu and Li, Xingyi and Cao, Zhiguo and Xian, Ke and Zhang, Jianming},
  title     = {Neural Video Depth Stabilizer},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year      = {2023}
}