Customized Integration

You can prototype a new RGB-D volumetric reconstruction algorithm with additional properties (e.g. semantic labels) while maintaining a reasonable performance. An example can be found at examples/python/t_reconstruction_system/integrate_custom.py.

Activation

The frustum block selection remains the same, but then we manually activate these blocks and obtain their buffer indices in the /tutorial/core/hashmap.ipynb:

60# examples/python/t_reconstruction_system/integrate_custom.py
61        # Get active frustum block coordinates from input
62        frustum_block_coords = vbg.compute_unique_block_coordinates(
63            depth, intrinsic, extrinsic, config.depth_scale, config.depth_max)
64        # Activate them in the underlying hash map (may have been inserted)
65        vbg.hashmap().activate(frustum_block_coords)
66
67        # Find buf indices in the underlying engine
68        buf_indices, masks = vbg.hashmap().find(frustum_block_coords)

Voxel Indices

We can then unroll voxel indices in these blocks into a flattened array, along with their corresponding voxel coordinates.

72# examples/python/t_reconstruction_system/integrate_custom.py
73        voxel_coords, voxel_indices = vbg.voxel_coordinates_and_flattened_indices(
74            buf_indices)

Up to now we have finished preparation. Then we can perform customized geometry transformation in the Tensor interface, with the same fashion as we conduct in numpy or pytorch.

Geometry transformation

We first transform the voxel coordinates to the frame’s coordinate system, project them to the image space, and filter out-of-bound correspondences:

80# examples/python/t_reconstruction_system/integrate_custom.py
81        extrinsic_dev = extrinsic.to(device, o3c.float32)
82        xyz = extrinsic_dev[:3, :3] @ voxel_coords.T() + extrinsic_dev[:3, 3:]
83
84        intrinsic_dev = intrinsic.to(device, o3c.float32)
85        uvd = intrinsic_dev @ xyz
86        d = uvd[2]
87        u = (uvd[0] / d).round().to(o3c.int64)
88        v = (uvd[1] / d).round().to(o3c.int64)
89        o3d.core.cuda.synchronize()
90        end = time.time()
91
92        start = time.time()
93        mask_proj = (d > 0) & (u >= 0) & (v >= 0) & (u < depth.columns) & (
94            v < depth.rows)
95
96        v_proj = v[mask_proj]
97        u_proj = u[mask_proj]
98        d_proj = d[mask_proj]

Customized integration

With the data association, we are able to conduct integration. In this example, we show the conventional TSDF integration written in vectorized Python code:

  • Read the associated RGB-D properties from the color/depth images at the associated u, v indices;

  • Read the voxels from the voxel buffer arrays (vbg.attribute) at masked voxel_indices;

  • Perform in-place modification

 98# examples/python/t_reconstruction_system/integrate_custom.py
 99        depth_readings = depth.as_tensor()[v_proj, u_proj, 0].to(
100            o3c.float32) / config.depth_scale
101        sdf = depth_readings - d_proj
102
103        mask_inlier = (depth_readings > 0) \
104            & (depth_readings < config.depth_max) \
105            & (sdf >= -trunc)
106
107        sdf[sdf >= trunc] = trunc
108        sdf = sdf / trunc
109        weight = vbg.attribute('weight').reshape((-1, 1))
110        tsdf = vbg.attribute('tsdf').reshape((-1, 1))
111
112        valid_voxel_indices = voxel_indices[mask_proj][mask_inlier]
113        w = weight[valid_voxel_indices]
114        wp = w + 1
115
116        tsdf[valid_voxel_indices] \
117            = (tsdf[valid_voxel_indices] * w +
118               sdf[mask_inlier].reshape(w.shape)) / (wp)
119        if config.integrate_color:
120            color = o3d.t.io.read_image(color_file_names[i]).to(device)
121            color_readings = color.as_tensor()[v_proj, u_proj].to(o3c.float32)
122
123            color = vbg.attribute('color').reshape((-1, 3))
124            color[valid_voxel_indices] \
125                = (color[valid_voxel_indices] * w +
126                         color_readings[mask_inlier]) / (wp)
127
128        weight[valid_voxel_indices] = wp

You may follow the example and adapt it to your customized properties. Open3D supports conversion from and to PyTorch tensors without memory any copy, see /tutorial/core/tensor.ipynb#PyTorch-I/O-with-DLPack-memory-map. This can be use to leverage PyTorch’s capabilities such as automatic differentiation and other operators.