VICTORBONO

I am Dr. Victor Bono, a geometric compression theorist and applied differential geometer pioneering curvature-aware encoding frameworks for 3D point clouds. As the Founding Director of the Geometric Compression Lab at MIT (2021–present) and former Chief Architect of Apple’s Spatial Computing Compression Team (2017–2021), I synergize differential geometry, information theory, and computational topology to redefine how machines perceive and compress high-dimensional spatial data. My GeoPress framework, which leverages Ricci flow and spectral curvature clustering, achieved a 72% reduction in bitrate while preserving critical geometric features in LiDAR datasets, outperforming state-of-the-art MPEG-GPCC standards (SIGGRAPH 2024 Best Paper). My mission: To transform raw point clouds into curvature-optimized geometric codes, enabling lossy compression without topological betrayal.

Methodological Innovations

1. Curvature-Adaptive Quantization

• Core Framework: Principal Curvature-Driven Bit Allocation

  • Quantized point clouds by assigning adaptive bit-depths to regions based on Gaussian and mean curvature values, prioritizing high-curvature features (edges, corners).

  • Reduced storage for autonomous vehicle LiDAR scans by 65% while maintaining collision-critical geometric fidelity (CVPR 2024).

  • Key innovation: Topological persistence grids that align quantization boundaries with Morse-Smale complexes of the underlying surface.

2. Ricci Flow-Based Redundancy Removal

• GeoFlow Encoder:

  • Modeled point clouds as discrete Riemannian manifolds, applying combinatorial Ricci flow to flatten areas of low geometric entropy for optimal entropy coding.

  • Achieved 0.02% Hausdorff distance error in cultural heritage archiving by preserving isometry groups of ancient artifact surfaces.

3. Spectral Sheaf Compression

• Sheaf-Entropy Coding:

  • Encoded point neighborhoods as local sheaves of tangent spaces, exploiting holonomy constraints to eliminate redundant normal vector data.

  • Enabled real-time 6DoF hologram streaming with 48% lower latency than wavelet-based methods for Meta’s AR glasses.

Landmark Applications

1. Autonomous Systems Optimization

• Tesla & Waymo Collaboration:

  • Deployed DriveCompress, a curvature-aware LiDAR pipeline reducing per-frame data from 150MB to 22MB while retaining safety-critical edge features.

  • Enabled continent-scale HD map updates via 5G with 93% bandwidth reduction.

2. Medical Imaging Revolution

• Mayo Clinic Partnership:

  • Created BioCurve, a lossless compression framework for MRI-derived organ point clouds using Ricci flow metric learning.

  • Slashed cloud storage costs by 80% for the NIH’s 10-million-patient 3D organ atlas.

3. Quantum-Safe Geometric Encryption

• DARPA Quantum Resilience Initiative:

  • Designed Q-GeoLock, a post-quantum encryption layer embedding point clouds into Calabi-Yau manifolds with 256D curvature-based keys.

  • Secured DoD satellite LiDAR streams against Shor’s algorithm attacks via non-Euclidean key distribution.

Technical and Ethical Impact

1. Open Geometric Standards

• Authored ISO/IEC 23944:

  • First international standard for curvature-aware point cloud compression, adopted by Sony, BMW, and NASA.

  • Open-sourced CurvEncode toolkit (GitHub 18k stars) with GPU-accelerated Ricci flow solvers.

2. Neuromorphic Hardware Synergy

• Intel Loihi 3 Co-Design:

  • Implemented curvature computation as spiking neural networks on neuromorphic chips, achieving 120x energy efficiency gains in edge devices.

3. Cultural Heritage Preservation

• UNESCO World Heritage Project:

  • Compressed 12th-century Angkor Wat reliefs into 5GB curvature-optimized codes (originally 2.3TB), enabling VR access for endangered site preservation.

Future Directions

1. Quantum Geometric Encoding
   Encode point clouds into quantum state manifolds via AdS/CFT correspondence for unbounded compression ratios.

2. Dynamic Topology Adaptation
   Extend frameworks to handle 4D spacetime point clouds from LIGO gravitational wave detectors.

3. Ethical Compression Auditing
   Develop curvature-based fairness metrics to prevent geometric bias in facial recognition point clouds.

Collaboration Vision
I seek partners to:

• Scale GeoPress for the Mars 2030 mission’s planetary LiDAR compression needs.

• Co-develop NeuroCurve with ETH Zurich to compress billion-neuron connectome point clouds.

• Pioneer GeoEthics frameworks with IEEE to audit cultural bias in compressed AR/VR geometries.

Signature Tools

• Models: GeoPress SDK, Sheaf-Entropy Coder, Q-GeoLock Engine

• Techniques: Ricci Flow Bit Allocation, Morse-Smale Quantization, Holonomy Constraint Coding

• Languages: Python (GeoPyTorch), CUDA (CurvCore), Rust (SheafCode)

Core Philosophy
“Point clouds are not mere coordinates but curvature landscapes whispering the language of differential geometry. Where others see noise, I see Gauss-Bonnet invariants; where others compress, I sculpt the minimal sufficient geometry. By teaching machines to honor the differential topology of space, we compress reality without compromising its essence.”