Non-line-of-sight (NLOS) imaging is the problem of reconstructing properties of scenes occluded from a sensor, using measurements of light that indirectly travels from the occluded scene to the sensor through intermediate diffuse reflections. We introduce an analysis-by-synthesis framework that can reconstruct complex shape and reflectance of an NLOS object. Our framework deviates from prior work on NLOS reconstruction, by directly optimizing for a surface representation of the NLOS object, in place of commonly employed volumetric representations. At the core of our framework is a new rendering formulation that efficiently computes derivatives of radiometric measurements with respect to NLOS geometry and reflectance, while accurately modeling the underlying light transport physics. By coupling this with stochastic optimization and geometry processing techniques, we are able to reconstruct NLOS surface at a level of detail significantly exceeding what is possible with previous volumetric reconstruction methods.

Non-line-of-sight (NLOS) imaging utilizes the full 5D light transient measurements to reconstruct scenes beyond the cameras field of view. Mathematically, this requires solving an elliptical tomography problem that unmixes the shape and albedo from spatially-multiplexed measurements of the NLOS scene. In this paper, we propose a new approach for NLOS imaging by studying the properties of first-returning photons from three-bounce light paths. We show that the times of flight of first-returning photons are dependent only on the geometry of the NLOS scene and each observation is almost always generated from a single NLOS scene point. Exploiting these properties, we derive a space carving algorithm for NLOS scenes. In addition, by assuming local planarity, we derive an algorithm to localize NLOS scene points in 3D and estimate their surface normals. Our methods do not require either the full transient measurements or solving the hard elliptical tomography problem. We demonstrate the effectiveness of our methods through simulations as well as real data captured from a SPAD sensor.

Computer vision and image-based inference have predominantly focused on extracting scene information by assuming that the camera measures direct light transport (i.e., single-bounce light paths). As a consequence, strong multi-bounce effects are treated typically as sources of noise and, in many scenarios, the presence of such effects can result in gross errors in the estimates of shape and reflectance. This paper provides the theoretical and algorithmic foundations for shape and reflectance estimation from two-bounce light transients, i.e., scenarios where photons from a light source interact with the scene exactly twice before reaching the sensor We derive sufficient conditions for exact recovery of shape and reflectance given lengths and intensities associated with two-bounce light paths. We also develop algorithms for recovery of shape and reflectance, and validate these on a range of simulated scenes.

We address the following problem in refractive shape estimation: given a single light-ray correspondence, what shape information of the transparent object is revealed along the path of the light-ray, assuming that the light-ray refracts twice. We answer this question in the form of two depth-normal ambiguities. First, specifying the surface normal at which refraction occurs constrains the depth to a unique value. Second, specifying the depth at which refraction occurs constrains the surface normal to lie on a 1D curve. These two depth-normal ambiguities are fundamental to shape estimation of transparent objects and can be used to derive additional properties. For example, we show that correspondences from three light-rays passing through a point are needed to correctly estimate its surface normal. Another contribution of this work is that we can reduce the number of views required to reconstruct an object by enforcing shape models. We demonstrate this property on real data where we reconstruct shape of an object, with light-rays observed from a single view, by enforcing a locally planar shape model.

Given a query image containing the object of interest (OOI), we propose a novel learning framework for retrieving relevant frames from the input video sequence. While techniques based on object matching have been applied to solve this task, their performance would be typically limited due to the lack of capabilities in handling variations in visual appearances of the OOI across video frames. Our proposed framework can be viewed as a weakly supervised approach, which only requires a small number of (randomly selected) relevant and irrelevant frames from the input video for performing satisfactory retrieval performance. By utilizing frame-level label information of such video frames together with the query image, we propose a novel query-adaptive multiple instance learning algorithm, which exploits the visual appearance information of the OOI from the query and that of the aforementioned video frames. As a result, the derived learning model would exhibit additional discriminating abilities while retrieving relevant instances. Experiments on two real-world video data sets would confirm the effectiveness and robustness of our proposed approach.

While bag-of-features (BOF) models have been widely applied for addressing image retrieval problems, the resulting performance is typically limited due to its disregard of spatial information of local image descriptors (and the associated visual words). In this paper, we present a novel spatial pooling scheme, called extended bag-of-features (EBOF), for solving the above task. Besides improving image representation capability, the incorporation of the our EBOF model with a proposed circular-correlation based similarity measure allows us to perform translation, rotation, and scale-invariant image retrieval. We conduct experiments on two benchmark image datasets, and the performance confirms the effectiveness and robustness of our proposed approach.