Coordinate network or implicit neural representation (INR) is a fast-emerging
method for encoding natural signals (such as images and videos) with the
benefits of a compact neural representation. While numerous methods have been
proposed to increase the encoding capabilities of an INR, an often overlooked
aspect is the inference efficiency, usually measured in multiply-accumulate
(MAC) count. This is particularly critical in use cases where inference
throughput is greatly limited by hardware constraints. To this end, we propose
the Activation-Sharing Multi-Resolution (ASMR) coordinate network that combines
multi-resolution coordinate decomposition with hierarchical modulations.
Specifically, an ASMR model enables the sharing of activations across grids of
the data. This largely decouples its inference cost from its depth which is
directly correlated to its reconstruction capability, and renders a near O(1)
inference complexity irrespective of the number of layers. Experiments show
that ASMR can reduce the MAC of a vanilla SIREN model by up to 500x while
achieving an even higher reconstruction quality than its SIREN baseline.

我们提出了激活共享多分辨率（ASMR）坐标网络，通过多分辨率坐标分解和分层调制，实现了激活在数据网格之间的共享，从而实现了近似 O (1) 的推理复杂度，大大减少了硬件约束下的推理效率。实验证明，ASMR 能够将普通 SIREN 模型的 MAC 计数降低多达 500 倍，同时实现更高的重建质量。

ASMR: 高效推理的激活共享多分辨率坐标网络

ASMR: Activation-sharing Multi-resolution Coordinate Networks For  Efficient Inference

The test-time optimization of scene flow - using a coordinate network as a
neural prior - has gained popularity due to its simplicity, lack of dataset
bias, and state-of-the-art performance. We observe, however, that although
coordinate networks capture general motions by implicitly regularizing the
scene flow predictions to be spatially smooth, the neural prior by itself is
unable to identify the underlying multi-body rigid motions present in
real-world data. To address this, we show that multi-body rigidity can be
achieved without the cumbersome and brittle strategy of constraining the
$SE(3)$ parameters of each rigid body as done in previous works. This is
achieved by regularizing the scene flow optimization to encourage isometry in
flow predictions for rigid bodies. This strategy enables multi-body rigidity in
scene flow while maintaining a continuous flow field, hence allowing dense
long-term scene flow integration across a sequence of point clouds. We conduct
extensive experiments on real-world datasets and demonstrate that our approach
outperforms the state-of-the-art in 3D scene flow and long-term point-wise 4D
trajectory prediction. The code is available at:
\href{https://github.com/kavisha725/MBNSF}{this https URL}.

我们展示了通过鼓励刚体流预测的等距性来实现多体刚性，从而在维持连续流场的同时允许通过一系列点云进行稠密的长期场景流积分。