Structure matters
Point clouds become more compressible when their physical or acquisition structure is made visible to the codec.
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3D data · Representation research · ZFP
Point-Cloud Compression
I investigated preconditioning techniques for 3D point clouds to improve their compressibility—starting with image-based representations and progressing to structure-aware ZFP experiments.
Point cloudsImage codingZFPLZ4LASzipExperimental evaluation
Method 1 encodes coordinates into image channels, then lets a lossless image codec do the work. Stanford Bunny, 35,947 vertices.
435.289 kB → 388.656 kB · −10.71% · median XYZ error 0.0617
00 / Interactive experiment
What happens when 3D coordinates are treated like image bytes?
This clean reproduction uses the 35,947-vertex Stanford Bunny. The original float32 XYZ payload is reshaped into an image-compatible byte layout, encoded, decoded, and rebuilt as a point cloud. Rotate the result to see how lossy image compression can leave shadows or aliases of the geometry.
Interactive representation experiment
See image coding alter a 3D point cloud.
Rotate the Stanford Bunny, compare the original XYZ cloud against the PNG- and JPEG-decoded payloads, and overlay all three to inspect geometric shadows and aliases.
Active view 00 · Original 35,947 float32 XYZ points.
Loading the Bunny comparison…
Two fingers to rotate · pinch to zoom
PNG reconstructed this payload bit-for-bit. JPEG quality 100 changed 42.4% of payload bytes; its median XYZ error was 0.0617 in the source coordinate units.
01 / What I am investigating
Can representation engineering make irregular points easier to compress?
A point cloud is often stored as an unordered list of XYZ values. Generic compressors see neighbouring rows in memory, not neighbouring points in space. My work tests whether image layouts, ordering, spherical coordinates, compact indices, and controlled precision can expose enough structure for numerical compressors to become effective.
ReduceStorage and transmission load
PreserveUseful 3D geometry
AccountDecoder metadata and point order
ExplainWhere each saving actually comes from
02 / Research problem
A smaller stream is meaningful only when the decoded cloud remains useful.
LiDAR produces large spatial datasets for autonomous driving, mapping, digital twins, and robotics. Compression can reduce storage and network demand, but file size alone is an incomplete metric. A fair experiment must also measure geometric fidelity, attribute recovery, ordering requirements, decoder completeness, and runtime.
Change one representation decision
Each experiment isolates image layout, ordering, score, index coding, quantization, or numerical compression.
Count the recoverable result
Payload, framing, permutations, precision, reconstruction error, and decoder requirements are evaluated together.
03 / Method 1
Image-based byte wrapping: a deliberately simple first probe.
Image codecs may find byte-level repetition
If XYZ bytes are reshaped into an image carrier, PNG or JPEG may exploit local patterns even without understanding geometry.
PCD → image-compatible bytes → image codec → PCD
The binary XYZ payload is reshaped into an image-compatible byte layout. PNG is tested losslessly; JPEG quality 100 is tested as an explicitly lossy control.
Compare bytes and rebuild the cloud
Image wrapping reduced the stored payload by 10.71% with PNG and 3.93% with JPEG q100. The clean Bunny reproduction then exposes the reconstruction consequences.
Image wrapping reduced the stored payload
What this shows. PNG found more byte-level redundancy than JPEG q100, while the interactive Bunny makes the geometric cost of the lossy path visible.
04 / Method 2
Order-aware preconditioning: sorting has a hidden recovery cost.
Place similar values together
Sorting by radial distance or a structural score should make neighbouring numerical values easier for compression codecs to encode.
Compress payload and permutation
Original-order, distance-sorted, and score-sorted XYZ streams are compared. Sorted variants also store the permutation needed to restore point order.
A component improved; the archive did not
Sorting improved one component of the encoded stream, but the permutation needed to restore point order cost more than the component saved. The complete order-recoverable archive was larger than the baseline.
05 / Method 3
Score-guided probing: ask which points are easier to encode.
A structural score may stratify compressibility
Points ordered by a recovered log-cardinality score may reveal regions that a numerical codec encodes more efficiently.
Remove one point and recompress
All points, the lower-score half, and the higher-score half are evaluated along a controlled removal sequence with signed byte changes.
The score was informative, not sufficient
The score did separate easier from harder subsets, but neither subset compressed below its raw size. Informative as a diagnostic, not sufficient as a codec — and the one-by-one recompression cost grows roughly quadratically.
06 / Method 4
Iterative midpoint-delta coding: the strongest exact custom primitive.
Angle dictionaries create compact integer structure
Quantized spherical angles repeat. Replacing them with indices should expose smaller, more regular values.
Recenter, store magnitudes, pack signs
Phi and theta indices are repeatedly centred, converted to absolute deltas, and paired with explicit sign bits until four-bit magnitudes remain.
The smallest self-contained encoding of the six tested
The iterative midpoint-delta representation with explicit sign packing produced the smallest self-contained encoding of the six exact variants tested, with exact index recovery.
07 / Method 5
Quantized spherical hybrid: combine codecs by stream type.
No single codec must encode everything
Distances, angle dictionaries, index deltas, and signs have different statistical structure and can use different encoders.
Spherical coordinates + RLE + ZFP + LZ4
XYZ is quantized in spherical form; repeated distances are run-length coded; angle indices use the midpoint-delta transform; numerical and byte streams are encoded separately.
Separate quantization from coding gain
Quantization supplied the larger first reduction; stream-specific custom coding added a second, smaller one. Separating the two contributions was the point of the experiment.
08 / Method 6
Direct reversible ZFP: the baseline that explains the research problem.
Test ZFP without preprocessing
If a raw XYZ matrix already contains sufficient numerical correlation, reversible ZFP should reduce it without a representation change.
Compress and verify bit-for-bit recovery
The raw float64 coordinate matrix is compressed directly using reversible ZFP and decoded exactly.
The unordered matrix expanded rather than compressed
Applying reversible ZFP directly to the unordered coordinate matrix produced a stream larger than the original payload. A rectangular array in memory is not a smooth spatial field — which is exactly what motivates the representation study.
09 / Methods tried alongside
The broader experiment record supplied baselines, controls, and alternative directions.
Point-cloud-specific codecs as baselines
LASzip/LAZ and Draco provided domain-specific reference points against which the custom experiments were measured.
Image-channel representations
Mapping coordinate axes into image channels, beyond the single-carrier layout shown in Method 1.
Ordering and local-search approaches
Iterative reordering strategies aimed at bringing spatially related points into a more regular sequence before coding.
Structure-aware lossy reductions
Local filtering and smoothing approaches trading geometric fidelity for compressibility.
10 / Why it matters
Point-cloud compression remains an application-dependent systems problem.
Large 3D streams affect logging, mapping, remote robotics, simulation, and continuous model development. Current reference directions solve different parts of the problem rather than producing one universal winner.
MPEG G-PCC
Directly codes sparse 3D geometry and attributes. Its generality brings configuration, complexity, and rate–distortion choices.
MPEG V-PCC
Uses the video ecosystem for dynamic volumetric content, but projection and patch processing add artifacts and overhead.
Google Draco
Efficiently transports meshes and point clouds, while quantization and graphics-oriented assumptions must match the downstream task.
RIDDLE · Jiffy
Exploit acquisition order and temporal range-image structure, which is powerful but sensor- and representation-dependent.
SCP
Uses spherical LiDAR structure with learned compression; model cost, deployment complexity, and domain transfer remain practical considerations.
ZFP
Excels on structured numerical fields, but unordered particle lists do not naturally supply the smooth neighbourhoods it expects.
Collective gap
No single method simultaneously optimizes sparse and dense geometry, attributes, temporal prediction, exact order, random access, streaming latency, bounded error, and downstream perception quality. The central engineering task is to match representation, fidelity budget, and access pattern to the application—and measure the complete recoverable system.