[NuRec] Failing to reproduce r2b_galileo 3D Reconstruction

Omniverse Core Platform NuRec
1

Operating System:
Linux
Kit Template:
USD Composer
GPU Hardware:
40 series
GPU Driver:
Latest

Hello,

I am currently running the NuRec pipeline described here:

Strictly following the documented specifications and parameters.

Using the official dataset r2b_galileo

Environment

  • Container: nvcr.io/nvidia/isaac/ros:noble-ros2_jazzy_d3e84470d576702a380478a513fb3fc6-amd64
  • nvblox installed following the official instructions

Step 4 – Mesh Generation with nvblox

Command used:

/packages/nvblox/build/nvblox/executables/fuse_cusfm \
    --color_image_dir images/ \
    --depth_image_dir depth/ \
    --frames_meta_file cusfm/kpmap/keyframes/frames_meta.json \
    --save_2d_occupancy_map_path nvblox_mesh/occupancy_map \
    --mapping_type_dynamic \
    --projective_integrator_max_integration_distance_m=2.5 \
    --esdf_slice_min_height=0.09 \
    --esdf_slice_max_height=0.65 \
    --esdf_slice_height=0.3 \
    --mesh_output_path nvblox_mesh/nvblox_mesh.ply \
    --nouse_2d_esdf_mode \
    --fit_to_z0 2>&1 | tee logs/step4.log

Output Generated

nvblox_mesh/
├── nvblox_mesh.ply
├── occupancy_map.png
├── occupancy_map.yaml
└── T_world_to_z0.json

Issue

The generated occupancy_map.png has significantly less detail compared to the official result provided by NVIDIA for the same dataset:

Generated by reproducing the pipeline:

occupancymap
occupancymap256×160 1.75 KB

Official reference (on HuggingFace):

occupancymap
occupancymap792×856 9.29 KB

This discrepancy propagates to Step 5, resulting in a much lower-quality 3D reconstruction compared to the official demo.
The behavior is similar to the issue discussed here:

What I Tried

  • Adjusted multiple nvblox parameters (integration distance, ESDF slicing bounds, voxel_size, etc.)
  • Some improvements were observed, but results are still far from the official output

Reproducibility Data (Logs + Outputs)

I have collected:

  • Full outputs and logs for all pipeline steps
  • Several resulting occupancy maps for comparison

These are available here:

Questions

  1. What exact parameters/configuration were used to generate the official r2b_galileo reconstruction (especially for nvblox / Step 4)?

  2. Are there additional preprocessing steps or implicit assumptions (e.g., filtering, coordinate normalization, depth scaling) not documented in the pipeline?

  3. For custom datasets, what is the recommended methodology to systematically tune nvblox parameters beyond trial-and-error? Are they related to the camera or the recorded scene?

    • Are there guidelines based on sensor characteristics, scene scale, or motion profile?
    • Any diagnostic metrics or intermediate outputs that should be monitored?

Any guidance would be appreciated, especially if there is a reference configuration or reproducible setup matching the official results.

Thanks.

2

Thank you for your post. I will reach out to a NuRec expert and have them get back to you.

3

From https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-NuRec/blob/main/nova_carter-galileo/occupancy_map.yaml

image: “occupancy_map.png”
mode: “trinary”
resolution: 0.02 # ← this is the nvblox voxel_size
origin: [-8.32, -5.92, 0]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.25

Following command I use with https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-NuRec/blob/main/nova_carter-galileo/raw_images.zip

fuse_cusfm
–color_image_dir /workspace/images/
–depth_image_dir /workspace/depth/
–frames_meta_file /workspace/cusfm/kpmap/keyframes/frames_meta.json
–save_2d_occupancy_map_path /workspace/nvblox_mesh/occupancy_map
–mesh_output_path /workspace/nvblox_mesh/nvblox_mesh.ply
–mapping_type_dynamic
–voxel_size=0.02
–projective_integrator_max_integration_distance_m=5.0
–esdf_slice_min_height=0.09
–esdf_slice_max_height=0.65
–esdf_slice_height=0.3
–nouse_2d_esdf_mode
–fit_to_z0