2026-09-20T08:14:45Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1629662025-10-07T04:14:41Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Sitzmann, Vincent
Xu, Daniel
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-10-06T17:37:13Z
2025-10-06T17:37:13Z
2025-05
2025-06-23T14:04:21.058Z
https://hdl.handle.net/1721.1/162966
We develop a differentiable rendering method for recovering 3D meshes of scenes from 2D images. Unlike existing approaches, our method does not rely on a differentiable renderers and is compatible with any standard mesh rasterizer. To our knowledge, it is the first mesh-based differentiable rendering method that is not reliant the use of visibility masks entirely. Beyond these conceptual advancements, we implemented a set of highly optimized kernels that enable efficient scene representation on a sparse voxel grid, effectively overcoming the cubic scaling bottleneck faced by similar methods. These innovations result in promising performance on unbounded real-world scenes with complex backgrounds.
M.Eng.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Triangle Splatting
Thesis
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Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
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<Abstract>We develop a differentiable rendering method for recovering 3D meshes of scenes from 2D images. Unlike existing approaches, our method does not rely on a differentiable renderers and is compatible with any standard mesh rasterizer. To our knowledge, it is the first mesh-based differentiable rendering method that is not reliant the use of visibility masks entirely. Beyond these conceptual advancements, we implemented a set of highly optimized kernels that enable efficient scene representation on a sparse voxel grid, effectively overcoming the cubic scaling bottleneck faced by similar methods. These innovations result in promising performance on unbounded real-world scenes with complex backgrounds.</Abstract>
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