Neural Implicit Probability Fields for Lossless Pointmap Compression

Pointmaps have recently emerged as a new geometric output of feed-forward 3D reconstruction models, creating a new target for 3D data compression. Their high spatial resolution and coordinate precision, however, incur substantial lossless storage costs. We propose Neural Implicit Probability Fields, a lossless pointmap codec that uses an implicit neural representation (INR) as an entropy model rather than a signal reconstructor. From a compression perspective, backpropagation can be viewed as compressing pointmap-specific statistical structure into the parameters of a compact neural probability field. To make this idea practical, we combine reversible geometric prediction with neural probability modeling, allowing the INR to focus on the remaining uncertainty rather than reconstructing the signal itself. The resulting codec preserves the native pixel-to-point correspondence and supports exact 16-bit reconstruction, while explicitly accounting for the transmitted neural model. Across diverse domains relevant to world modeling and embodied intelligence, our method reduces BPP by 1.4–30.3% over the strongest grid-preserving baselines, demonstrating that neural optimization can provide an effective mechanism for lossless compression of high-precision 3D geometry.