Ray Casting in a Voxel Block Grid
Note
This is NOT ray casting for triangle meshes. Please refer to /python_api/open3d.t.geometry.RayCastingScene.rst for that use case.
Ray casting can be performed in a voxel block grid to generate depth and color images at specific view points without extracting the entire surface. It is useful for frame-to-model tracking, and for differentiable volume rendering.
We provide optimized conventional rendering, and basic support for customized rendering that may be used in differentiable rendering. An example can be found at examples/python/t_reconstruction_system/ray_casting.py
.
Conventional rendering
From a reconstructed voxel block grid from TSDF Integration, we can efficiently render the scene given the input depth as a rough range estimate.
76 parser = ConfigParser()
77 ],
78 depth_scale=config.depth_scale,
79 depth_min=config.depth_min,
80 depth_max=config.depth_max,
81 weight_threshold=1,
82 range_map_down_factor=8)
83
84 fig, axs = plt.subplots(2, 2)
85
86 # Colorized depth
87 colorized_depth = o3d.t.geometry.Image(result['depth']).colorize_depth(
88 config.depth_scale, config.depth_min, config.depth_max)
89
90 # Render color via indexing
91 vbg_color = vbg.attribute('color').reshape((-1, 3))
92 nb_indices = result['index'].reshape((-1))
The results could be directly obtained and visualized by
90 parser = ConfigParser()
91 vbg_color = vbg.attribute('color').reshape((-1, 3))
92 nb_interp_ratio = result['interp_ratio'].reshape((-1, 1))
93 nb_colors = vbg_color[nb_indices] * nb_interp_ratio
94 sum_colors = nb_colors.reshape((depth.rows, depth.columns, 8, 3)).sum(
95 axs[1, 0].set_title('color via kernel')
96
97 axs[1, 1].imshow(sum_colors.cpu().numpy())
98 axs[1, 1].set_title('color via indexing')
99
100 plt.tight_layout()
101 plt.show()
Customized rendering
In customized rendering, we manually perform trilinear-interpolation by accessing properties at 8 nearest neighbor voxels with respect to the found surface point per pixel:
97 parser = ConfigParser()
98 axs[0, 0].imshow(colorized_depth.as_tensor().cpu().numpy())
99 axs[0, 0].set_title('depth')
100
101 axs[0, 1].imshow(result['normal'].cpu().numpy())
102 axs[0, 1].set_title('normal')
103
Since the output is rendered via indices, the rendering process could be rewritten in differentiable engines like PyTorch seamlessly via /tutorial/core/tensor.ipynb#PyTorch-I/O-with-DLPack-memory-map.