Concept Graph & Resume using Claude 3 Opus | Chat GPT4o | Llama 3:
Resume:
1.- SplatNet is a network architecture for processing 3D point clouds directly.
2.- Standard CNNs are not well-suited for sparse, unstructured point cloud data.
3.- Previous workarounds like voxelization or multi-view 2D projections have limitations.
4.- SplatNet enables flexible filter receptive fields to capture information at multiple scales.
5.- It also allows seamless joint processing of 2D image and 3D point cloud data.
6.- The key building block is Bilateral Convolution Layer (BCL).
7.- BCL efficiently filters sparse points via interpolation to/from vertices of a lattice structure.
8.- Permutohedral lattice scales better to higher dimensions than a regular grid.
9.- BCL specifies two feature sets: point features (what) and lattice features (where).
10.- Lattice features can use different scales to control receptive field size.
11.- BCL allows input and output points to be at different locations.
12.- Variants BCL-2D-to-3D and BCL-3D-to-2D project information between 2D and 3D.
13.- SplatNet-3D has 1x1 convolutions, a sequence of BCLs with increasing lattice scales, and concatenation.
14.- SplatNet-2D-3D adds 2D CNN branches with projection between 2D and 3D features.
15.- SplatNet-3D outperforms previous methods on Rimont 2014 building facade segmentation benchmark.
16.- The joint SplatNet-2D-3D further improves both 3D and 2D prediction results.
17.- On ShapeNet part segmentation, SplatNet matches state-of-the-art and joint 2D-3D helps further.
18.- Using XYZ+normals as 6D lattice features gives flexibility but only marginal gains here.
19.- The approach enables efficient point cloud computation, flexible receptive fields, and seamless 2D-3D integration.
20.- Code is available online.
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