Hybrid-PCGC is a point cloud geometry compression method that blends learned generalization with implicit neural representations. It treats compression as a joint geometry-and-model bitrate allocation problem, optimizing instance‑specific weight offsets as transmitted variables. The approach separates dataset‑agnostic prior modeling from instance‑specific adaptation using a Frozen General Module, a Dual Trainable Adaptive Module, an Adaptive Scope Controller, and a Supervised Offset Compression Module, achieving significant bitrate reductions over existing methods.
arXiv:2610.15092v1 Announce Type: cross Abstract: Quantization plays a crucial role in efficiently deploying deep learning models on resource-constrained devices. Post-training quantization does not...
arXiv:2608. 07106v1 Announce Type: new Abstract: Deploying three-dimensional deep learning frameworks to low-power embedded processors is bottlenecked by the unstructured nature of spatial data and the resource-intensive distance sorting algorithms often used before neural network inference.
arXiv:2606. 00404v1 Announce Type: cross Abstract: Implicit neural representations (INRs) model a signal as a continuous coordinate-to-value function.
arXiv:2610.35489v1 Announce Type: new Abstract: Deploying large vision-language models (LVLMs) on resource-constrained embodied agents for 3D visual question answering necessitates cloud-edge collabo...
arXiv:2608. 12239v1 Announce Type: cross Abstract: Use this plain-text version for the arXiv abstract field: Learned image compression (LIC) models achieve strong rate-distortion performance but are hindered by high computational complexity and encoding-decoding mismatches across heterogeneous hardware platforms.
arXiv:2606. 04115v1 Announce Type: cross Abstract: Quantizing large language models (LLMs) to low-precision floating-point representations is central to efficient deployment, yet applying a single bit-width uniformly across all layers is sub-optimal in terms of both performance and accuracy.
Constrained Dynamic Gaussian Splatting (CDGS) addresses the memory challenges of Dynamic Gaussian Splatting by framing reconstruction as a budget‑constrained optimization problem. It introduces a differentiable budget controller that fuses geometric, motion, and perceptual cues to enforce a strict, user‑defined Gaussian budget during training. CDGS further splits the representation into static and dynamic branches, allocates capacity based on motion complexity, and employs a three‑phase training strategy and hybrid compression to achieve high‑quality rendering with over 3× model compression compared to Ex4DGS.
arXiv:2607. 21985v1 Announce Type: cross Abstract: The increasing deployment of large language models (LLMs) has magnified the computational and memory bottlenecks of autoregressive decoding, where low compute intensity and bandwidth-bound kernels dominate inference cost.
arXiv:2606. 03940v1 Announce Type: cross Abstract: In robotics systems, vast amounts of visual data are easily captured at high resolution using low-cost, low-power hardware.
Use this plain-text version for the arXiv abstract field: Learned image compression (LIC) models achieve strong rate-distortion performance but are hindered by high computational complexity and encoding-decoding mismatches across heterogeneous hardware platforms. Uniform fixed-precision quantization alleviates these issues but suffers severe quality degradation at low bit widths because it ignores differences in the quantization sensitivities of individual layers.
GS‑Net is a lightweight plug‑and‑play module that expands sparse Structure‑from‑Motion point clouds into dense Gaussian primitives, enabling cross‑sensor view synthesis for autonomous driving. It learns a generalizable initialization for 3D Gaussian Splatting, improving rendering quality for both interpolated and extrapolated camera viewpoints. The authors introduce CARLA‑NVS, a benchmark with 12 uniformly spaced cameras, and show that GS‑Net outperforms standard 3DGS by 2.08 dB PSNR on interpolated views and 1.86 dB on extrapolated views while being 50× faster to initialize.
The paper introduces QMapAnything, a post‑training quantization framework tailored for feed‑forward geometry transformers that use Transformers to predict camera poses, depth, and structure in a single pass. It addresses the challenge that heterogeneous token types share a single activation quantization grid, which can cause high‑leverage tokens to dominate outputs. By treating rotation and smoothing as token‑blind reparameterizations and defining a statistic γ to gauge when differentiated bias correction is needed, the method achieves W4A8 quantization that closely matches full‑precision performance while W8A8 reduces peak memory by 2.2–3.1× and speeds up inference by up to 3.1× on real hardware.